From 71174023723277836e67be9c04f0022dd386be21 Mon Sep 17 00:00:00 2001 From: AntreasAntoniou Date: Thu, 13 Sep 2018 02:35:42 +0100 Subject: [PATCH 01/22] lab2 --- data/ccpp_data.npz | Bin 0 -> 350007 bytes mlp/data_providers.py | 97 ++- mlp/errors.py | 44 ++ mlp/initialisers.py | 65 ++ mlp/layers.py | 139 ++++ mlp/learning_rules.py | 162 +++++ mlp/models.py | 67 ++ mlp/optimisers.py | 134 ++++ notebooks/01_Introduction.ipynb | 184 +++++- notebooks/02_Single_layer_models.ipynb | 861 +++++++++++++++++++++++++ 10 files changed, 1715 insertions(+), 38 deletions(-) create mode 100644 data/ccpp_data.npz create mode 100644 mlp/errors.py create mode 100644 mlp/initialisers.py create mode 100644 mlp/layers.py create mode 100644 mlp/learning_rules.py create mode 100644 mlp/models.py create mode 100644 mlp/optimisers.py create mode 100644 notebooks/02_Single_layer_models.ipynb diff --git a/data/ccpp_data.npz b/data/ccpp_data.npz new file mode 100644 index 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""" - raise NotImplementedError() + one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes)) + one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1 + return one_of_k_targets class MetOfficeDataProvider(DataProvider): @@ -164,7 +166,7 @@ class MetOfficeDataProvider(DataProvider): def __init__(self, window_size, batch_size=10, max_num_batches=-1, shuffle_order=True, rng=None): - """Create a new Met Offfice data provider object. + """Create a new Met Office data provider object. Args: window_size (int): Size of windows to split weather time series @@ -180,27 +182,74 @@ def __init__(self, window_size, batch_size=10, max_num_batches=-1, the data before each epoch. rng (RandomState): A seeded random number generator. """ - self.window_size = window_size - assert window_size > 1, 'window_size must be at least 2.' data_path = os.path.join( os.environ['MLP_DATA_DIR'], 'HadSSP_daily_qc.txt') assert os.path.isfile(data_path), ( 'Data file does not exist at expected path: ' + data_path ) - # load raw data from text file - # ... + raw = np.loadtxt(data_path, skiprows=3, usecols=range(2, 32)) + assert window_size > 1, 'window_size must be at least 2.' + self.window_size = window_size # filter out all missing datapoints and flatten to a vector - # ... + filtered = raw[raw >= 0].flatten() # normalise data to zero mean, unit standard deviation - # ... - # convert from flat sequence to windowed data - # ... + mean = np.mean(filtered) + std = np.std(filtered) + normalised = (filtered - mean) / std + # create a view on to array corresponding to a rolling window + shape = (normalised.shape[-1] - self.window_size + 1, self.window_size) + strides = normalised.strides + (normalised.strides[-1],) + windowed = np.lib.stride_tricks.as_strided( + normalised, shape=shape, strides=strides) # inputs are first (window_size - 1) entries in windows - # inputs = ... + inputs = windowed[:, :-1] # targets are last entry in windows - # targets = ... - # initialise base class with inputs and targets arrays - # super(MetOfficeDataProvider, self).__init__( - # inputs, targets, batch_size, max_num_batches, shuffle_order, rng) - def __next__(self): - return self.next() \ No newline at end of file + targets = windowed[:, -1] + super(MetOfficeDataProvider, self).__init__( + inputs, targets, batch_size, max_num_batches, shuffle_order, rng) + +class CCPPDataProvider(DataProvider): + + def __init__(self, which_set='train', input_dims=None, batch_size=10, + max_num_batches=-1, shuffle_order=True, rng=None): + """Create a new Combined Cycle Power Plant data provider object. + + Args: + which_set: One of 'train' or 'valid'. Determines which portion of + data this object should provide. + input_dims: Which of the four input dimension to use. If `None` all + are used. If an iterable of integers are provided (consisting + of a subset of {0, 1, 2, 3}) then only the corresponding + input dimensions are included. + batch_size (int): Number of data points to include in each batch. + max_num_batches (int): Maximum number of batches to iterate over + in an epoch. If `max_num_batches * batch_size > num_data` then + only as many batches as the data can be split into will be + used. If set to -1 all of the data will be used. + shuffle_order (bool): Whether to randomly permute the order of + the data before each epoch. + rng (RandomState): A seeded random number generator. + """ + data_path = os.path.join( + os.environ['MLP_DATA_DIR'], 'ccpp_data.npz') + assert os.path.isfile(data_path), ( + 'Data file does not exist at expected path: ' + data_path + ) + # check a valid which_set was provided + assert which_set in ['train', 'valid'], ( + 'Expected which_set to be either train or valid ' + 'Got {0}'.format(which_set) + ) + # check input_dims are valid + if not input_dims is not None: + input_dims = set(input_dims) + assert input_dims.issubset({0, 1, 2, 3}), ( + 'input_dims should be a subset of {0, 1, 2, 3}' + ) + loaded = np.load(data_path) + inputs = loaded[which_set + '_inputs'] + if input_dims is not None: + inputs = inputs[:, input_dims] + targets = loaded[which_set + '_targets'] + super(CCPPDataProvider, self).__init__( + inputs, targets, batch_size, max_num_batches, shuffle_order, rng) diff --git a/mlp/errors.py b/mlp/errors.py new file mode 100644 index 0000000..712fe59 --- /dev/null +++ b/mlp/errors.py @@ -0,0 +1,44 @@ +# -*- coding: utf-8 -*- +"""Error functions. + +This module defines error functions, with the aim of model training being to +minimise the error function given a set of inputs and target outputs. + +The error functions will typically measure some concept of distance between the +model outputs and target outputs, averaged over all data points in the data set +or batch. +""" + +import numpy as np + + +class SumOfSquaredDiffsError(object): + """Sum of squared differences (squared Euclidean distance) error.""" + + def __call__(self, outputs, targets): + """Calculates error function given a batch of outputs and targets. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Scalar error function value. + """ + raise NotImplementedError() + + def grad(self, outputs, targets): + """Calculates gradient of error function with respect to outputs. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Gradient of error function with respect to outputs. This should be + an array of shape (batch_size, output_dim). + """ + raise NotImplementedError() + + def __repr__(self): + return 'SumOfSquaredDiffsError' diff --git a/mlp/initialisers.py b/mlp/initialisers.py new file mode 100644 index 0000000..243adc2 --- /dev/null +++ b/mlp/initialisers.py @@ -0,0 +1,65 @@ +# -*- coding: utf-8 -*- +"""Parameter initialisers. + +This module defines classes to initialise the parameters in a layer. +""" + +import numpy as np +from mlp import DEFAULT_SEED + + +class ConstantInit(object): + """Constant parameter initialiser.""" + + def __init__(self, value): + """Construct a constant parameter initialiser. + + Args: + value: Value to initialise parameter to. + """ + self.value = value + + def __call__(self, shape): + return np.ones(shape=shape) * self.value + + +class UniformInit(object): + """Random uniform parameter initialiser.""" + + def __init__(self, low, high, rng=None): + """Construct a random uniform parameter initialiser. + + Args: + low: Lower bound of interval to sample from. + high: Upper bound of interval to sample from. + rng (RandomState): Seeded random number generator. + """ + self.low = low + self.high = high + if rng is None: + rng = np.random.RandomState(DEFAULT_SEED) + self.rng = rng + + def __call__(self, shape): + return self.rng.uniform(low=self.low, high=self.high, size=shape) + + +class NormalInit(object): + """Random normal parameter initialiser.""" + + def __init__(self, mean, std, rng=None): + """Construct a random uniform parameter initialiser. + + Args: + mean: Mean of distribution to sample from. + std: Standard deviation of distribution to sample from. + rng (RandomState): Seeded random number generator. + """ + self.mean = mean + self.std = std + if rng is None: + rng = np.random.RandomState(DEFAULT_SEED) + self.rng = rng + + def __call__(self, shape): + return self.rng.normal(loc=self.mean, scale=self.std, size=shape) diff --git a/mlp/layers.py b/mlp/layers.py new file mode 100644 index 0000000..e2e871b --- /dev/null +++ b/mlp/layers.py @@ -0,0 +1,139 @@ +# -*- coding: utf-8 -*- +"""Layer definitions. + +This module defines classes which encapsulate a single layer. + +These layers map input activations to output activation with the `fprop` +method and map gradients with repsect to outputs to gradients with respect to +their inputs with the `bprop` method. + +Some layers will have learnable parameters and so will additionally define +methods for getting and setting parameter and calculating gradients with +respect to the layer parameters. +""" + +import numpy as np +import mlp.initialisers as init + + +class Layer(object): + """Abstract class defining the interface for a layer.""" + + def fprop(self, inputs): + """Forward propagates activations through the layer transformation. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + + Returns: + outputs: Array of layer outputs of shape (batch_size, output_dim). + """ + raise NotImplementedError() + + def bprop(self, inputs, outputs, grads_wrt_outputs): + """Back propagates gradients through a layer. + + Given gradients with respect to the outputs of the layer calculates the + gradients with respect to the layer inputs. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + outputs: Array of layer outputs calculated in forward pass of + shape (batch_size, output_dim). + grads_wrt_outputs: Array of gradients with respect to the layer + outputs of shape (batch_size, output_dim). + + Returns: + Array of gradients with respect to the layer inputs of shape + (batch_size, input_dim). + """ + raise NotImplementedError() + + +class LayerWithParameters(Layer): + """Abstract class defining the interface for a layer with parameters.""" + + def grads_wrt_params(self, inputs, grads_wrt_outputs): + """Calculates gradients with respect to layer parameters. + + Args: + inputs: Array of inputs to layer of shape (batch_size, input_dim). + grads_wrt_to_outputs: Array of gradients with respect to the layer + outputs of shape (batch_size, output_dim). + + Returns: + List of arrays of gradients with respect to the layer parameters + with parameter gradients appearing in same order in tuple as + returned from `get_params` method. + """ + raise NotImplementedError() + + @property + def params(self): + """Returns a list of parameters of layer. + + Returns: + List of current parameter values. + """ + raise NotImplementedError() + + +class AffineLayer(LayerWithParameters): + """Layer implementing an affine tranformation of its inputs. + + This layer is parameterised by a weight matrix and bias vector. + """ + + def __init__(self, input_dim, output_dim, + weights_initialiser=init.UniformInit(-0.1, 0.1), + biases_initialiser=init.ConstantInit(0.), + weights_cost=None, biases_cost=None): + """Initialises a parameterised affine layer. + + Args: + input_dim (int): Dimension of inputs to the layer. + output_dim (int): Dimension of the layer outputs. + weights_initialiser: Initialiser for the weight parameters. + biases_initialiser: Initialiser for the bias parameters. + """ + self.input_dim = input_dim + self.output_dim = output_dim + self.weights = weights_initialiser((self.output_dim, self.input_dim)) + self.biases = biases_initialiser(self.output_dim) + + def fprop(self, inputs): + """Forward propagates activations through the layer transformation. + + For inputs `x`, outputs `y`, weights `W` and biases `b` the layer + corresponds to `y = W.dot(x) + b`. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + + Returns: + outputs: Array of layer outputs of shape (batch_size, output_dim). + """ + raise NotImplementedError() + + def grads_wrt_params(self, inputs, grads_wrt_outputs): + """Calculates gradients with respect to layer parameters. + + Args: + inputs: array of inputs to layer of shape (batch_size, input_dim) + grads_wrt_to_outputs: array of gradients with respect to the layer + outputs of shape (batch_size, output_dim) + + Returns: + list of arrays of gradients with respect to the layer parameters + `[grads_wrt_weights, grads_wrt_biases]`. + """ + raise NotImplementedError() + + @property + def params(self): + """A list of layer parameter values: `[weights, biases]`.""" + return [self.weights, self.biases] + + def __repr__(self): + return 'AffineLayer(input_dim={0}, output_dim={1})'.format( + self.input_dim, self.output_dim) diff --git a/mlp/learning_rules.py b/mlp/learning_rules.py new file mode 100644 index 0000000..22f2bcb --- /dev/null +++ b/mlp/learning_rules.py @@ -0,0 +1,162 @@ +# -*- coding: utf-8 -*- +"""Learning rules. + +This module contains classes implementing gradient based learning rules. +""" + +import numpy as np + + +class GradientDescentLearningRule(object): + """Simple (stochastic) gradient descent learning rule. + + For a scalar error function `E(p[0], p_[1] ... )` of some set of + potentially multidimensional parameters this attempts to find a local + minimum of the loss function by applying updates to each parameter of the + form + + p[i] := p[i] - learning_rate * dE/dp[i] + + With `learning_rate` a positive scaling parameter. + + The error function used in successive applications of these updates may be + a stochastic estimator of the true error function (e.g. when the error with + respect to only a subset of data-points is calculated) in which case this + will correspond to a stochastic gradient descent learning rule. + """ + + def __init__(self, learning_rate=1e-3): + """Creates a new learning rule object. + + Args: + learning_rate: A postive scalar to scale gradient updates to the + parameters by. This needs to be carefully set - if too large + the learning dynamic will be unstable and may diverge, while + if set too small learning will proceed very slowly. + + """ + assert learning_rate > 0., 'learning_rate should be positive.' + self.learning_rate = learning_rate + + def initialise(self, params): + """Initialises the state of the learning rule for a set or parameters. + + This must be called before `update_params` is first called. + + Args: + params: A list of the parameters to be optimised. Note these will + be updated *in-place* to avoid reallocating arrays on each + update. + """ + self.params = params + + def reset(self): + """Resets any additional state variables to their intial values. + + For this learning rule there are no additional state variables so we + do nothing here. + """ + pass + + def update_params(self, grads_wrt_params): + """Applies a single gradient descent update to all parameters. + + All parameter updates are performed using in-place operations and so + nothing is returned. + + Args: + grads_wrt_params: A list of gradients of the scalar loss function + with respect to each of the parameters passed to `initialise` + previously, with this list expected to be in the same order. + """ + for param, grad in zip(self.params, grads_wrt_params): + param -= self.learning_rate * grad + + +class MomentumLearningRule(GradientDescentLearningRule): + """Gradient descent with momentum learning rule. + + This extends the basic gradient learning rule by introducing extra + momentum state variables for each parameter. These can help the learning + dynamic help overcome shallow local minima and speed convergence when + making multiple successive steps in a similar direction in parameter space. + + For parameter p[i] and corresponding momentum m[i] the updates for a + scalar loss function `L` are of the form + + m[i] := mom_coeff * m[i] - learning_rate * dL/dp[i] + p[i] := p[i] + m[i] + + with `learning_rate` a positive scaling parameter for the gradient updates + and `mom_coeff` a value in [0, 1] that determines how much 'friction' there + is the system and so how quickly previous momentum contributions decay. + """ + + def __init__(self, learning_rate=1e-3, mom_coeff=0.9): + """Creates a new learning rule object. + + Args: + learning_rate: A postive scalar to scale gradient updates to the + parameters by. This needs to be carefully set - if too large + the learning dynamic will be unstable and may diverge, while + if set too small learning will proceed very slowly. + mom_coeff: A scalar in the range [0, 1] inclusive. This determines + the contribution of the previous momentum value to the value + after each update. If equal to 0 the momentum is set to exactly + the negative scaled gradient each update and so this rule + collapses to standard gradient descent. If equal to 1 the + momentum will just be decremented by the scaled gradient at + each update. This is equivalent to simulating the dynamic in + a frictionless system. Due to energy conservation the loss + of 'potential energy' as the dynamics moves down the loss + function surface will lead to an increasingly large 'kinetic + energy' and so speed, meaning the updates will become + increasingly large, potentially unstably so. Typically a value + less than but close to 1 will avoid these issues and cause the + dynamic to converge to a local minima where the gradients are + by definition zero. + """ + super(MomentumLearningRule, self).__init__(learning_rate) + assert mom_coeff >= 0. and mom_coeff <= 1., ( + 'mom_coeff should be in the range [0, 1].' + ) + self.mom_coeff = mom_coeff + + def initialise(self, params): + """Initialises the state of the learning rule for a set or parameters. + + This must be called before `update_params` is first called. + + Args: + params: A list of the parameters to be optimised. Note these will + be updated *in-place* to avoid reallocating arrays on each + update. + """ + super(MomentumLearningRule, self).initialise(params) + self.moms = [] + for param in self.params: + self.moms.append(np.zeros_like(param)) + + def reset(self): + """Resets any additional state variables to their intial values. + + For this learning rule this corresponds to zeroing all the momenta. + """ + for mom in zip(self.moms): + mom *= 0. + + def update_params(self, grads_wrt_params): + """Applies a single update to all parameters. + + All parameter updates are performed using in-place operations and so + nothing is returned. + + Args: + grads_wrt_params: A list of gradients of the scalar loss function + with respect to each of the parameters passed to `initialise` + previously, with this list expected to be in the same order. + """ + for param, mom, grad in zip(self.params, self.moms, grads_wrt_params): + mom *= self.mom_coeff + mom -= self.learning_rate * grad + param += mom diff --git a/mlp/models.py b/mlp/models.py new file mode 100644 index 0000000..86a0472 --- /dev/null +++ b/mlp/models.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +"""Model definitions. + +This module implements objects encapsulating learnable models of input-output +relationships. The model objects implement methods for forward propagating +the inputs through the transformation(s) defined by the model to produce +outputs (and intermediate states) and for calculating gradients of scalar +functions of the outputs with respect to the model parameters. +""" + +from mlp.layers import LayerWithParameters + + +class SingleLayerModel(object): + """A model consisting of a single transformation layer.""" + + def __init__(self, layer): + """Create a new single layer model instance. + + Args: + layer: The layer object defining the model architecture. + """ + self.layer = layer + + @property + def params(self): + """A list of all of the parameters of the model.""" + return self.layer.params + + def fprop(self, inputs): + """Calculate the model outputs corresponding to a batch of inputs. + + Args: + inputs: Batch of inputs to the model. + + Returns: + List which is a concatenation of the model inputs and model + outputs, this being done for consistency of the interface with + multi-layer models for which `fprop` returns a list of + activations through all immediate layers of the model and including + the inputs and outputs. + """ + activations = [inputs, self.layer.fprop(inputs)] + return activations + + def grads_wrt_params(self, activations, grads_wrt_outputs): + """Calculates gradients with respect to the model parameters. + + Args: + activations: List of all activations from forward pass through + model using `fprop`. + grads_wrt_outputs: Gradient with respect to the model outputs of + the scalar function parameter gradients are being calculated + for. + + Returns: + List of gradients of the scalar function with respect to all model + parameters. + """ + return self.layer.grads_wrt_params(activations[0], grads_wrt_outputs) + + def params_cost(self): + """Calculates the parameter dependent cost term of the model.""" + return self.layer.params_cost() + + def __repr__(self): + return 'SingleLayerModel(' + str(layer) + ')' diff --git a/mlp/optimisers.py b/mlp/optimisers.py new file mode 100644 index 0000000..01dd8b6 --- /dev/null +++ b/mlp/optimisers.py @@ -0,0 +1,134 @@ +# -*- coding: utf-8 -*- +"""Model optimisers. + +This module contains objects implementing (batched) stochastic gradient descent +based optimisation of models. +""" + +import time +import logging +from collections import OrderedDict +import numpy as np + + +logger = logging.getLogger(__name__) + + +class Optimiser(object): + """Basic model optimiser.""" + + def __init__(self, model, error, learning_rule, train_dataset, + valid_dataset=None, data_monitors=None): + """Create a new optimiser instance. + + Args: + model: The model to optimise. + error: The scalar error function to minimise. + learning_rule: Gradient based learning rule to use to minimise + error. + train_dataset: Data provider for training set data batches. + valid_dataset: Data provider for validation set data batches. + data_monitors: Dictionary of functions evaluated on targets and + model outputs (averaged across both full training and + validation data sets) to monitor during training in addition + to the error. Keys should correspond to a string label for + the statistic being evaluated. + """ + self.model = model + self.error = error + self.learning_rule = learning_rule + self.learning_rule.initialise(self.model.params) + self.train_dataset = train_dataset + self.valid_dataset = valid_dataset + self.data_monitors = OrderedDict([('error', error)]) + if data_monitors is not None: + self.data_monitors.update(data_monitors) + + def do_training_epoch(self): + """Do a single training epoch. + + This iterates through all batches in training dataset, for each + calculating the gradient of the estimated error given the batch with + respect to all the model parameters and then updates the model + parameters according to the learning rule. + """ + for inputs_batch, targets_batch in self.train_dataset: + activations = self.model.fprop(inputs_batch) + grads_wrt_outputs = self.error.grad(activations[-1], targets_batch) + grads_wrt_params = self.model.grads_wrt_params( + activations, grads_wrt_outputs) + self.learning_rule.update_params(grads_wrt_params) + + def eval_monitors(self, dataset, label): + """Evaluates the monitors for the given dataset. + + Args: + dataset: Dataset to perform evaluation with. + label: Tag to add to end of monitor keys to identify dataset. + + Returns: + OrderedDict of monitor values evaluated on dataset. + """ + data_mon_vals = OrderedDict([(key + label, 0.) for key + in self.data_monitors.keys()]) + for inputs_batch, targets_batch in dataset: + activations = self.model.fprop(inputs_batch) + for key, data_monitor in self.data_monitors.items(): + data_mon_vals[key + label] += data_monitor( + activations[-1], targets_batch) + for key, data_monitor in self.data_monitors.items(): + data_mon_vals[key + label] /= dataset.num_batches + return data_mon_vals + + def get_epoch_stats(self): + """Computes training statistics for an epoch. + + Returns: + An OrderedDict with keys corresponding to the statistic labels and + values corresponding to the value of the statistic. + """ + epoch_stats = OrderedDict() + epoch_stats.update(self.eval_monitors(self.train_dataset, '(train)')) + if self.valid_dataset is not None: + epoch_stats.update(self.eval_monitors( + self.valid_dataset, '(valid)')) + return epoch_stats + + def log_stats(self, epoch, epoch_time, stats): + """Outputs stats for a training epoch to a logger. + + Args: + epoch (int): Epoch counter. + epoch_time: Time taken in seconds for the epoch to complete. + stats: Monitored stats for the epoch. + """ + logger.info('Epoch {0}: {1:.1f}s to complete\n {2}'.format( + epoch, epoch_time, + ', '.join(['{0}={1:.2e}'.format(k, v) for (k, v) in stats.items()]) + )) + + def train(self, num_epochs, stats_interval=5): + """Trains a model for a set number of epochs. + + Args: + num_epochs: Number of epochs (complete passes through trainin + dataset) to train for. + stats_interval: Training statistics will be recorded and logged + every `stats_interval` epochs. + + Returns: + Tuple with first value being an array of training run statistics + and the second being a dict mapping the labels for the statistics + recorded to their column index in the array. + """ + run_stats = [list(self.get_epoch_stats().values())] + for epoch in range(1, num_epochs + 1): + start_time = time.clock() + self.do_training_epoch() + epoch_time = time.clock() - start_time + if epoch % stats_interval == 0: + stats = self.get_epoch_stats() + self.log_stats(epoch, epoch_time, stats) + run_stats.append(list(stats.values())) + return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())} + diff --git a/notebooks/01_Introduction.ipynb b/notebooks/01_Introduction.ipynb index 7e65b1b..0fcf77b 100644 --- a/notebooks/01_Introduction.ipynb +++ b/notebooks/01_Introduction.ipynb @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "nbpresent": { "id": "2bced39d-ae3a-4603-ac94-fbb6a6283a96" @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "nbpresent": { "id": "978c1095-a9ce-4626-a113-e0be5fe51ecb" @@ -329,7 +329,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Image target: [9]\n" + "Image target: [[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" ] }, { @@ -348,7 +348,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Image target: [8]\n" + "Image target: [[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n" ] } ], @@ -392,15 +392,102 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# write your code here for iterating over five batches of \n", - "# 100 data points each and displaying as 10x10 grids\n", + "def show_batch_of_images(img_batch, fig_size=(3, 3)):\n", + " fig = plt.figure(figsize=fig_size)\n", + " batch_size, im_height, im_width = img_batch.shape\n", + " # calculate no. columns per grid row to give square grid\n", + " grid_size = int(batch_size**0.5)\n", + " # intialise empty array to tile image grid into\n", + " tiled = np.empty((im_height * grid_size, \n", + " im_width * batch_size // grid_size))\n", + " # iterate over images in batch + indexes within batch\n", + " for i, img in enumerate(img_batch):\n", + " # calculate grid row and column indices\n", + " r, c = i % grid_size, i // grid_size\n", + " tiled[r * im_height:(r + 1) * im_height, \n", + " c * im_height:(c + 1) * im_height] = img\n", + " ax = fig.add_subplot(111)\n", + " ax.imshow(tiled, cmap='Greys')\n", + " ax.axis('off')\n", + " fig.tight_layout()\n", + " plt.show()\n", + " return fig, ax\n", + "\n", + "batch_size = 100\n", + "num_batches = 5\n", + "\n", + "mnist_dp = data_providers.MNISTDataProvider(\n", + " which_set='valid', batch_size=batch_size, \n", + " max_num_batches=num_batches, shuffle_order=True)\n", "\n", - "def show_batch_of_images(img_batch):\n", - " raise NotImplementedError('Write me!')" + "for inputs, target in mnist_dp:\n", + " # reshape inputs from batch of vectors to batch of 2D arrays (images)\n", + " _ = show_batch_of_images(inputs.reshape((batch_size, 28, 28)))" ] }, { @@ -434,9 +521,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n", + "[[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n", + "[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n", + "[[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]\n", + "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n" + ] + } + ], "source": [ "mnist_dp = data_providers.MNISTDataProvider(\n", " which_set='valid', batch_size=5, max_num_batches=5, shuffle_order=False)\n", @@ -483,13 +602,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "nbpresent": { "id": "c8553a56-9f25-4198-8a1a-d7e9572b4382" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "batch_size = 3\n", "for window_size in [2, 5, 10]:\n", diff --git a/notebooks/02_Single_layer_models.ipynb b/notebooks/02_Single_layer_models.ipynb new file mode 100644 index 0000000..35d0ae5 --- /dev/null +++ b/notebooks/02_Single_layer_models.ipynb @@ -0,0 +1,861 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Single layer models\n", + "\n", + "In this lab we will implement a single-layer network model consisting of solely of an affine transformation of the inputs. The relevant material for this was covered in [slides 12-23 of the first lecture](http://www.inf.ed.ac.uk/teaching/courses/mlp/2018-19/mlp01-intro.pdf). \n", + "\n", + "We will first implement the forward propagation of inputs to the network to produce predicted outputs. We will then move on to considering how to use gradients of an error function evaluated on the outputs to compute the gradients with respect to the model parameters to allow us to perform an iterative gradient-descent training procedure. In the final exercise you will use an interactive visualisation to explore the role of some of the different hyperparameters of gradient-descent based training methods.\n", + "\n", + "#### A note on random number generators\n", + "\n", + "It is generally a good practice (for machine learning applications **not** for cryptography!) to seed a pseudo-random number generator once at the beginning of each experiment. This makes it easier to reproduce results as the same random draws will produced each time the experiment is run (e.g. the same random initialisations used for parameters). Therefore generally when we need to generate random values during this course, we will create a seeded random number generator object as we do in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "seed = 27092016 \n", + "rng = np.random.RandomState(seed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1: linear and affine transforms\n", + "\n", + "Any *linear transform* (also called a linear map) of a finite-dimensional vector space can be parametrised by a matrix. So for example if we consider $\\boldsymbol{x} \\in \\mathbb{R}^{D}$ as the input space of a model with $D$ dimensional real-valued inputs, then a matrix $\\mathbf{W} \\in \\mathbb{R}^{K\\times D}$ can be used to define a prediction model consisting solely of a linear transform of the inputs\n", + "\n", + "\\begin{equation}\n", + " \\boldsymbol{y} = \\mathbf{W} \\boldsymbol{x}\n", + " \\qquad\n", + " \\Leftrightarrow\n", + " \\qquad\n", + " y_k = \\sum_{d=1}^D \\left( W_{kd} x_d \\right) \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n", + "\\end{equation}\n", + "\n", + "with here $\\boldsymbol{y} \\in \\mathbb{R}^K$ the $K$-dimensional real-valued output of the model. Geometrically we can think of a linear transform doing some combination of rotation, scaling, reflection and shearing of the input.\n", + "\n", + "An *affine transform* consists of a linear transform plus an additional translation parameterised by a vector $\\boldsymbol{b} \\in \\mathbb{R}^K$. A model consisting of an affine transformation of the inputs can then be defined as\n", + "\n", + "\\begin{equation}\n", + " \\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}\n", + " \\qquad\n", + " \\Leftrightarrow\n", + " \\qquad\n", + " y_k = \\sum_{d=1}^D \\left( W_{kd} x_d \\right) + b_k \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n", + "\\end{equation}\n", + "\n", + "In machine learning we will usually refer to the matrix $\\mathbf{W}$ as a *weight matrix* and the vector $\\boldsymbol{b}$ as a *bias vector*.\n", + "\n", + "Generally rather than working with a single data vector $\\boldsymbol{x}$ we will work with batches of datapoints $\\left\\lbrace \\boldsymbol{x}^{(b)}\\right\\rbrace_{b=1}^B$. We could calculate the outputs for each input in the batch sequentially\n", + "\n", + "\\begin{align}\n", + " \\boldsymbol{y}^{(1)} &= \\mathbf{W}\\boldsymbol{x}^{(1)} + \\boldsymbol{b}\\\\\n", + " \\boldsymbol{y}^{(2)} &= \\mathbf{W}\\boldsymbol{x}^{(2)} + \\boldsymbol{b}\\\\\n", + " \\dots &\\\\\n", + " \\boldsymbol{y}^{(B)} &= \\mathbf{W}\\boldsymbol{x}^{(B)} + \\boldsymbol{b}\\\\\n", + "\\end{align}\n", + "\n", + "by looping over each input in the batch and calculating the output. However in general loops in Python are slow (particularly compared to compiled and typed languages such as C). This is due at least in part to the large overhead in dynamically inferring variable types. In general therefore wherever possible we want to avoid having loops in which such overhead will become the dominant computational cost.\n", + "\n", + "For array based numerical operations, one way of overcoming this bottleneck is to *vectorise* operations. NumPy `ndarrays` are typed arrays for which operations such as basic elementwise arithmetic and linear algebra operations such as computing matrix-matrix or matrix-vector products are implemented by calls to highly-optimised compiled libraries. Therefore if you can implement code directly using NumPy operations on arrays rather than by looping over array elements it is often possible to make very substantial performance gains.\n", + "\n", + "As a simple example we can consider adding up two arrays `a` and `b` and writing the result to a third array `c`. First lets initialise `a` and `b` with arbitrary values by running the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "size = 1000\n", + "a = np.arange(size)\n", + "b = np.ones(size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's time how long it takes to add up each pair of values in the two array and write the results to a third array using a loop-based implementation. We will use the `%%timeit` magic briefly mentioned in the previous lab notebook specifying the number of times to loop the code as 100 and to give the best of 3 repeats. Run the cell below to get a print out of the average time taken." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.02 ms ± 27.3 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit -n 100 -r 3\n", + "c = np.empty(size)\n", + "for i in range(size):\n", + " c[i] = a[i] + b[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we will perform the corresponding summation with the overloaded addition operator of NumPy arrays. Again run the cell below to get a print out of the average time taken." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The slowest run took 7.12 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "4.66 µs ± 4.38 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit -n 100 -r 3\n", + "c = a + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first loop-based implementation should have taken on the order of milliseconds ($10^{-3}$s) while the vectorised implementation should have taken on the order of microseconds ($10^{-6}$s), i.e. a $\\sim1000\\times$ speedup. Hopefully this simple example should make it clear why we want to vectorise operations whenever possible!\n", + "\n", + "Getting back to our affine model, ideally rather than individually computing the output corresponding to each input we should compute the outputs for all inputs in a batch using a vectorised implementation. As you saw last week, data providers return batches of inputs as arrays of shape `(batch_size, input_dim)`. In the mathematical notation used earlier we can consider this as a matrix $\\mathbf{X}$ of dimensionality $B \\times D$, and in particular\n", + "\n", + "\\begin{equation}\n", + " \\mathbf{X} = \\left[ \\boldsymbol{x}^{(1)} ~ \\boldsymbol{x}^{(2)} ~ \\dots ~ \\boldsymbol{x}^{(B)} \\right]^\\mathrm{T}\n", + "\\end{equation}\n", + "\n", + "i.e. the $b^{\\textrm{th}}$ input vector $\\boldsymbol{x}^{(b)}$ corresponds to the $b^{\\textrm{th}}$ row of $\\mathbf{X}$. If we define the $B \\times K$ matrix of outputs $\\mathbf{Y}$ similarly as\n", + "\n", + "\\begin{equation}\n", + " \\mathbf{Y} = \\left[ \\boldsymbol{y}^{(1)} ~ \\boldsymbol{y}^{(2)} ~ \\dots ~ \\boldsymbol{y}^{(B)} \\right]^\\mathrm{T}\n", + "\\end{equation}\n", + "\n", + "then we can express the relationship between $\\mathbf{X}$ and $\\mathbf{Y}$ using [matrix multiplication](https://en.wikipedia.org/wiki/Matrix_multiplication) and addition as\n", + "\n", + "\\begin{equation}\n", + " \\mathbf{Y} = \\mathbf{X} \\mathbf{W}^\\mathrm{T} + \\mathbf{B}\n", + "\\end{equation}\n", + "\n", + "where $\\mathbf{B} = \\left[ \\boldsymbol{b} ~ \\boldsymbol{b} ~ \\dots ~ \\boldsymbol{b} \\right]^\\mathrm{T}$ i.e. a $B \\times K$ matrix with each row corresponding to the bias vector. The weight matrix needs to be transposed here as the inner dimensions of a matrix multiplication must match i.e. for $\\mathbf{C} = \\mathbf{A} \\mathbf{B}$ then if $\\mathbf{A}$ is of dimensionality $K \\times L$ and $\\mathbf{B}$ is of dimensionality $M \\times N$ then it must be the case that $L = M$ and $\\mathbf{C}$ will be of dimensionality $K \\times N$.\n", + "\n", + "The first exercise for this lab is to implement *forward propagation* for a single-layer model consisting of an affine transformation of the inputs in the `fprop` function given as skeleton code in the cell below. This should work for a batch of inputs of shape `(batch_size, input_dim)` producing a batch of outputs of shape `(batch_size, output_dim)`.\n", + " \n", + "You will probably want to use the NumPy `dot` function and [broadcasting features](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) to implement this efficiently. If you are not familiar with either / both of these you may wish to read the [hints](#Hints:-Using-the-dot-function-and-broadcasting) section below which gives some details on these before attempting the exercise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def fprop(inputs, weights, biases):\n", + " \"\"\"Forward propagates activations through the layer transformation.\n", + "\n", + " For inputs `x`, outputs `y`, weights `W` and biases `b` the layer\n", + " corresponds to `y = W x + b`.\n", + "\n", + " Args:\n", + " inputs: Array of layer inputs of shape (batch_size, input_dim).\n", + " weights: Array of weight parameters of shape \n", + " (output_dim, input_dim).\n", + " biases: Array of bias parameters of shape (output_dim, ).\n", + "\n", + " Returns:\n", + " outputs: Array of layer outputs of shape (batch_size, output_dim).\n", + " \"\"\"\n", + " raise NotImplementedError('Delete this raise statement and write your code here instead.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have implemented `fprop` in the cell above you can test your implementation by running the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = np.array([[0., -1., 2.], [-6., 3., 1.]])\n", + "weights = np.array([[2., -3., -1.], [-5., 7., 2.]])\n", + "biases = np.array([5., -3.])\n", + "true_outputs = np.array([[6., -6.], [-17., 50.]])\n", + "\n", + "if not np.allclose(fprop(inputs, weights, biases), true_outputs):\n", + " print('Wrong outputs computed.')\n", + "else:\n", + " print('All outputs correct!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hints: Using the `dot` function and broadcasting\n", + "\n", + "For those new to NumPy below are some details on the `dot` function and broadcasting feature of NumPy that you may want to use for implementing the first exercise. If you are already familiar with these and have already completed the first exercise you can move on straight to [second exercise](#Exercise-2:-visualising-random-models).\n", + "\n", + "#### `numpy.dot` function\n", + "\n", + "Matrix-matrix, matrix-vector and vector-vector (dot) products can all be computed in NumPy using the [`dot`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function. For example if `A` and `B` are both two dimensional arrays, then `C = np.dot(A, B)` or equivalently `C = A.dot(B)` will both compute the matrix product of `A` and `B` assuming `A` and `B` have compatible dimensions. Similarly if `a` and `b` are one dimensional arrays then `c = np.dot(a, b)` (which is equivalent to `c = a.dot(b)`) will compute the [scalar / dot product](https://en.wikipedia.org/wiki/Dot_product) of the two arrays. If `A` is a two-dimensional array and `b` a one-dimensional array `np.dot(A, b)` (which is equivalent to `A.dot(b)`) will compute the matrix-vector product of `A` and `b`. Examples of all three of these product types are shown in the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 6. 6. 6.]\n", + " [24. 24. 24.]\n", + " [42. 42. 42.]]\n", + "[[18. 24. 30.]\n", + " [18. 24. 30.]\n", + " [18. 24. 30.]]\n", + "[0.8 2.6 4.4]\n", + "[2.4 3. 3.6]\n", + "0.19999999999999998\n" + ] + } + ], + "source": [ + "# Initiliase arrays with arbitrary values\n", + "A = np.arange(9).reshape((3, 3))\n", + "B = np.ones((3, 3)) * 2\n", + "a = np.array([-1., 0., 1.])\n", + "b = np.array([0.1, 0.2, 0.3])\n", + "print(A.dot(B)) # Matrix-matrix product\n", + "print(B.dot(A)) # Reversed product of above A.dot(B) != B.dot(A) in general\n", + "print(A.dot(b)) # Matrix-vector product\n", + "print(b.dot(A)) # Again A.dot(b) != b.dot(A) unless A is symmetric i.e. A == A.T\n", + "print(a.dot(b)) # Vector-vector scalar product" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Broadcasting\n", + "\n", + "Another NumPy feature it will be helpful to get familiar with is [broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). Broadcasting allows you to apply operations to arrays of different shapes, for example to add a one-dimensional array to a two-dimensional array or multiply a multidimensional array by a scalar. The complete set of rules for broadcasting as explained in the official documentation page just linked to can sound a bit complex: you might find the [visual explanation on this page](http://www.scipy-lectures.org/intro/numpy/operations.html#broadcasting) more intuitive. The cell below gives a few examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.1 1.2]\n", + " [2.1 3.2]\n", + " [4.1 5.2]]\n", + "[[-1. 0.]\n", + " [ 2. 3.]\n", + " [ 5. 6.]]\n", + "[[0. 0.2]\n", + " [0.2 0.6]\n", + " [0.4 1. ]]\n" + ] + } + ], + "source": [ + "# Initiliase arrays with arbitrary values\n", + "A = np.arange(6).reshape((3, 2))\n", + "b = np.array([0.1, 0.2])\n", + "c = np.array([-1., 0., 1.])\n", + "print(A + b) # Add b elementwise to all rows of A\n", + "print((A.T + c).T) # Add b elementwise to all columns of A\n", + "print(A * b) # Multiply each row of A elementise by b " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2: visualising random models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this exercise you will use your `fprop` implementation to visualise the outputs of a single-layer affine transform model with two-dimensional inputs and a one-dimensional output. In this simple case we can visualise the joint input-output space on a 3D axis.\n", + "\n", + "For this task and the learning experiments later in the notebook we will use a regression dataset from the [UCI machine learning repository](http://archive.ics.uci.edu/ml/index.html). In particular we will use a version of the [Combined Cycle Power Plant dataset](http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant), where the task is to predict the energy output of a power plant given observations of the local ambient conditions (e.g. temperature, pressure and humidity).\n", + "\n", + "The original dataset has four input dimensions and a single target output dimension. We have preprocessed the dataset by [whitening](https://en.wikipedia.org/wiki/Whitening_transformation) it, a common preprocessing step. We will only use the first two dimensions of the whitened inputs (corresponding to the first two principal components of the inputs) so we can easily visualise the joint input-output space.\n", + "\n", + "The dataset has been wrapped in the `CCPPDataProvider` class in the `mlp.data_providers` module and the data included as a compressed file in the data directory as `ccpp_data.npz`. Running the cell below will initialise an instance of this class, get a single batch of inputs and outputs and import the necessary `matplotlib` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from mlp.data_providers import CCPPDataProvider\n", + "%matplotlib notebook\n", + "\n", + "data_provider = CCPPDataProvider(\n", + " which_set='train',\n", + " input_dims=[0, 1],\n", + " batch_size=5000, \n", + " max_num_batches=1, \n", + " shuffle_order=False\n", + ")\n", + "\n", + "input_dim, output_dim = 2, 1\n", + "\n", + "inputs, targets = data_provider.next()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we used the `%matplotlib notebook` magic command rather than the `%matplotlib inline` we used in the previous lab as this allows us to produce interactive 3D plots which you can rotate and zoom in/out by dragging with the mouse and scrolling the mouse-wheel respectively. Once you have finished interacting with a plot you can close it to produce a static inline plot using the button in the top-right corner.\n", + "\n", + "Now run the cell below to plot the predicted outputs of a randomly initialised model across the two dimensional input space as well as the true target outputs. This sort of visualisation can be a useful method (in low dimensions) to assess how well the model is likely to be able to fit the data and to judge appropriate initialisation scales for the parameters. Each time you re-run the cell a new set of random parameters will be sampled\n", + "\n", + "Some questions to consider:\n", + "\n", + " * How do the weights and bias initialisation scale affect the sort of predicted input-output relationships?\n", + " * Does the linear form of the model seem appropriate for the data here?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'fprop' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# Calculate predicted model outputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbiases\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# Plot target and predicted outputs against inputs on same axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'fprop' is not defined" + ] + } + ], + "source": [ + "weights_init_range = 0.5\n", + "biases_init_range = 0.1\n", + "\n", + "# Randomly initialise weights matrix\n", + "weights = rng.uniform(\n", + " low=-weights_init_range, \n", + " high=weights_init_range, \n", + " size=(output_dim, input_dim)\n", + ")\n", + "\n", + "# Randomly initialise biases vector\n", + "biases = rng.uniform(\n", + " low=-biases_init_range, \n", + " high=biases_init_range, \n", + " size=output_dim\n", + ")\n", + "# Calculate predicted model outputs\n", + "outputs = fprop(inputs, weights, biases)\n", + "\n", + "# Plot target and predicted outputs against inputs on same axis\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n", + "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n", + "ax.set_xlabel('Input dim 1')\n", + "ax.set_ylabel('Input dim 2')\n", + "ax.set_zlabel('Output')\n", + "ax.legend(['Targets', 'Predictions'], frameon=False)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3: computing the error function and its gradient\n", + "\n", + "Here we will consider the task of regression as covered in the first lecture slides. The aim in a regression problem is given inputs $\\left\\lbrace \\boldsymbol{x}^{(n)}\\right\\rbrace_{n=1}^N$ to produce outputs $\\left\\lbrace \\boldsymbol{y}^{(n)}\\right\\rbrace_{n=1}^N$ that are as 'close' as possible to a set of target outputs $\\left\\lbrace \\boldsymbol{t}^{(n)}\\right\\rbrace_{n=1}^N$. The measure of 'closeness' or distance between target and predicted outputs is a design choice. \n", + "\n", + "A very common choice is the squared Euclidean distance between the predicted and target outputs. This can be computed as the sum of the squared differences between each element in the target and predicted outputs. A common convention is to multiply this value by $\\frac{1}{2}$ as this gives a slightly nicer expression for the error gradient. The error for the $n^{\\textrm{th}}$ training example is then\n", + "\n", + "\\begin{equation}\n", + " E^{(n)} = \\frac{1}{2} \\sum_{k=1}^K \\left\\lbrace \\left( y^{(n)}_k - t^{(n)}_k \\right)^2 \\right\\rbrace.\n", + "\\end{equation}\n", + "\n", + "The overall error is then the *average* of this value across all training examples\n", + "\n", + "\\begin{equation}\n", + " \\bar{E} = \\frac{1}{N} \\sum_{n=1}^N \\left\\lbrace E^{(n)} \\right\\rbrace. \n", + "\\end{equation}\n", + "\n", + "*Note here we are using a slightly different convention from the lectures. There the overall error was considered to be the sum of the individual error terms rather than the mean. To differentiate between the two we will use $\\bar{E}$ to represent the average error here as opposed to sum of errors $E$ as used in the slides with $\\bar{E} = \\frac{E}{N}$. Normalising by the number of training examples is helpful to do in practice as this means we can more easily compare errors across data sets / batches of different sizes, and more importantly it means the size of our gradient updates will be independent of the number of training examples summed over.*\n", + "\n", + "The regression problem is then to find parameters of the model which minimise $\\bar{E}$. For our simple single-layer affine model here that corresponds to finding weights $\\mathbf{W}$ and biases $\\boldsymbol{b}$ which minimise $\\bar{E}$. \n", + "\n", + "As mentioned in the lecture, for this simple case there is actually a closed form solution for the optimal weights and bias parameters. This is the linear least-squares solution those doing MLPR will have come across.\n", + "\n", + "However in general we will be interested in models where closed form solutions do not exist. We will therefore generally use iterative, gradient descent based training methods to find parameters which (locally) minimise the error function. A basic requirement of being able to do gradient-descent based training is (unsuprisingly) the ability to evaluate gradients of the error function.\n", + "\n", + "In the next exercise we will consider how to calculate gradients of the error function with respect to the model parameters $\\mathbf{W}$ and $\\boldsymbol{b}$, but as a first step here we will consider the gradient of the error function with respect to the model outputs $\\left\\lbrace \\boldsymbol{y}^{(n)}\\right\\rbrace_{n=1}^N$. This can be written\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial \\bar{E}}{\\partial \\boldsymbol{y}^{(n)}} = \\frac{1}{N} \\left( \\boldsymbol{y}^{(n)} - \\boldsymbol{t}^{(n)} \\right)\n", + " \\qquad \\Leftrightarrow \\qquad\n", + " \\frac{\\partial \\bar{E}}{\\partial y^{(n)}_k} = \\frac{1}{N} \\left( y^{(n)}_k - t^{(n)}_k \\right) \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n", + "\\end{equation}\n", + "\n", + "i.e. the gradient of the error function with respect to the $n^{\\textrm{th}}$ model output is just the difference between the $n^{\\textrm{th}}$ model and target outputs, corresponding to the $\\boldsymbol{\\delta}^{(n)}$ terms mentioned in the lecture slides.\n", + "\n", + "The third exercise is, using the equations given above, to implement functions computing the mean sum of squared differences error and its gradient with respect to the model outputs. You should implement the functions using the provided skeleton definitions in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def error(outputs, targets):\n", + " \"\"\"Calculates error function given a batch of outputs and targets.\n", + "\n", + " Args:\n", + " outputs: Array of model outputs of shape (batch_size, output_dim).\n", + " targets: Array of target outputs of shape (batch_size, output_dim).\n", + "\n", + " Returns:\n", + " Scalar error function value.\n", + " \"\"\"\n", + " raise NotImplementedError('Delete this raise statement and write your code here instead.')\n", + " \n", + "def error_grad(outputs, targets):\n", + " \"\"\"Calculates gradient of error function with respect to model outputs.\n", + "\n", + " Args:\n", + " outputs: Array of model outputs of shape (batch_size, output_dim).\n", + " targets: Array of target outputs of shape (batch_size, output_dim).\n", + "\n", + " Returns:\n", + " Gradient of error function with respect to outputs.\n", + " This will be an array of shape (batch_size, output_dim).\n", + " \"\"\"\n", + " raise NotImplementedError('Delete this raise clause and write your code here instead.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check your implementation by running the test cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "outputs = np.array([[1., 2.], [-1., 0.], [6., -5.], [-1., 1.]])\n", + "targets = np.array([[0., 1.], [3., -2.], [7., -3.], [1., -2.]])\n", + "true_error = 5.\n", + "true_error_grad = np.array([[0.25, 0.25], [-1., 0.5], [-0.25, -0.5], [-0.5, 0.75]])\n", + "\n", + "if not error(outputs, targets) == true_error:\n", + " print('Error calculated incorrectly.')\n", + "elif not np.allclose(error_grad(outputs, targets), true_error_grad):\n", + " print('Error gradient calculated incorrectly.')\n", + "else:\n", + " print('Error function and gradient computed correctly!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 4: computing gradients with respect to the parameters\n", + "\n", + "In the previous exercise you implemented a function computing the gradient of the error function with respect to the model outputs. For gradient-descent based training, we need to be able to evaluate the gradient of the error function with respect to the model parameters.\n", + "\n", + "Using the [chain rule for derivatives](https://en.wikipedia.org/wiki/Chain_rule#Higher_dimensions) we can write the partial deriviative of the error function with respect to single elements of the weight matrix and bias vector as\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial E}{\\partial W_{kj}} = \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\frac{\\partial y^{(n)}_k}{\\partial W_{kj}} \\right\\rbrace\n", + " \\quad \\textrm{and} \\quad\n", + " \\frac{\\partial E}{\\partial b_k} = \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\frac{\\partial y^{(n)}_k}{\\partial b_k} \\right\\rbrace.\n", + "\\end{equation}\n", + "\n", + "From the definition of our model at the beginning we have \n", + "\n", + "\\begin{equation}\n", + " y^{(n)}_k = \\sum_{d=1}^D \\left\\lbrace W_{kd} x^{(n)}_d \\right\\rbrace + b_k\n", + " \\quad \\Rightarrow \\quad\n", + " \\frac{\\partial y^{(n)}_k}{\\partial W_{kj}} = x^{(n)}_j\n", + " \\quad \\textrm{and} \\quad\n", + " \\frac{\\partial y^{(n)}_k}{\\partial b_k} = 1.\n", + "\\end{equation}\n", + "\n", + "Putting this together we get that\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial E}{\\partial W_{kj}} = \n", + " \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} x^{(n)}_j \\right\\rbrace\n", + " \\quad \\textrm{and} \\quad\n", + " \\frac{\\partial E}{\\partial b_{k}} = \n", + " \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\right\\rbrace.\n", + "\\end{equation}\n", + "\n", + "Although this may seem a bit of a roundabout way to get to these results, this method of decomposing the error gradient with respect to the parameters in terms of the gradient of the error function with respect to the model outputs and the derivatives of the model outputs with respect to the model parameters, will be key when calculating the parameter gradients of more complex models later in the course.\n", + "\n", + "Your task in this exercise is to implement a function calculating the gradient of the error function with respect to the weight and bias parameters of the model given the already computed gradient of the error function with respect to the model outputs. You should implement this in the `grads_wrt_params` function in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def grads_wrt_params(inputs, grads_wrt_outputs):\n", + " \"\"\"Calculates gradients with respect to model parameters.\n", + "\n", + " Args:\n", + " inputs: array of inputs to model of shape (batch_size, input_dim)\n", + " grads_wrt_to_outputs: array of gradients of with respect to the model\n", + " outputs of shape (batch_size, output_dim).\n", + "\n", + " Returns:\n", + " list of arrays of gradients with respect to the model parameters\n", + " `[grads_wrt_weights, grads_wrt_biases]`.\n", + " \"\"\"\n", + " raise NotImplementedError('Delete this raise statement and write your code here instead.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check your implementation by running the test cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = np.array([[1., 2., 3.], [-1., 4., -9.]])\n", + "grads_wrt_outputs = np.array([[-1., 1.], [2., -3.]])\n", + "true_grads_wrt_weights = np.array([[-3., 6., -21.], [4., -10., 30.]])\n", + "true_grads_wrt_biases = np.array([1., -2.])\n", + "\n", + "grads_wrt_weights, grads_wrt_biases = grads_wrt_params(\n", + " inputs, grads_wrt_outputs)\n", + "\n", + "if not np.allclose(true_grads_wrt_weights, grads_wrt_weights):\n", + " print('Gradients with respect to weights incorrect.')\n", + "elif not np.allclose(true_grads_wrt_biases, grads_wrt_biases):\n", + " print('Gradients with respect to biases incorrect.')\n", + "else:\n", + " print('All parameter gradients calculated correctly!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 5: wrapping the functions into reusable components\n", + "\n", + "In exercises 1, 3 and 4 you implemented methods to compute the predicted outputs of our model, evaluate the error function and its gradient on the outputs and finally to calculate the gradients of the error with respect to the model parameters. Together they constitute all the basic ingredients we need to implement a gradient-descent based iterative learning procedure for the model.\n", + "\n", + "Although you could implement training code which directly uses the functions you defined, this would only be usable for this particular model architecture. In subsequent labs we will want to use the affine transform functions as the basis for more interesting multi-layer models. We will therefore wrap the implementations you just wrote in to reusable components that we can build more complex models with later in the course.\n", + "\n", + " * In the [`mlp.layers`](/edit/mlp/layers.py) module, use your implementations of `fprop` and `grad_wrt_params` above to implement the corresponding methods in the skeleton `AffineLayer` class provided.\n", + " * In the [`mlp.errors`](/edit/mlp/errors.py) module use your implementation of `error` and `error_grad` to implement the `__call__` and `grad` methods respectively of the skeleton `SumOfSquaredDiffsError` class provided. Note `__call__` is a special Python method that allows an object to be used with a function call syntax.\n", + "\n", + "Run the cell below to use your completed `AffineLayer` and `SumOfSquaredDiffsError` implementations to train a single-layer model using batch gradient descent on the CCPP dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlp.layers import AffineLayer\n", + "from mlp.errors import SumOfSquaredDiffsError\n", + "from mlp.models import SingleLayerModel\n", + "from mlp.initialisers import UniformInit, ConstantInit\n", + "from mlp.learning_rules import GradientDescentLearningRule\n", + "from mlp.optimisers import Optimiser\n", + "import logging\n", + "\n", + "# Seed a random number generator\n", + "seed = 27092016 \n", + "rng = np.random.RandomState(seed)\n", + "\n", + "# Set up a logger object to print info about the training run to stdout\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "logger.handlers = [logging.StreamHandler()]\n", + "\n", + "# Create data provider objects for the CCPP training set\n", + "train_data = CCPPDataProvider('train', [0, 1], batch_size=100, rng=rng)\n", + "input_dim, output_dim = 2, 1\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-0.1, 0.1]\n", + "param_init = UniformInit(-0.1, 0.1, rng=rng)\n", + "\n", + "# Create our single layer model\n", + "layer = AffineLayer(input_dim, output_dim, param_init, param_init)\n", + "model = SingleLayerModel(layer)\n", + "\n", + "# Initialise the error object\n", + "error = SumOfSquaredDiffsError()\n", + "\n", + "# Use a basic gradient descent learning rule with a small learning rate\n", + "learning_rule = GradientDescentLearningRule(learning_rate=1e-2)\n", + "\n", + "# Use the created objects to initialise a new Optimiser instance.\n", + "optimiser = Optimiser(model, error, learning_rule, train_data)\n", + "\n", + "# Run the optimiser for 5 epochs (full passes through the training set)\n", + "# printing statistics every epoch.\n", + "stats, keys = optimiser.train(num_epochs=10, stats_interval=1)\n", + "\n", + "# Plot the change in the error over training.\n", + "fig = plt.figure(figsize=(8, 4))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(np.arange(1, stats.shape[0] + 1), stats[:, keys['error(train)']])\n", + "ax.set_xlabel('Epoch number')\n", + "ax.set_ylabel('Error')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using similar code to previously we can now visualise the joint input-output space for the trained model. If you implemented the required methods correctly you should now see a much improved fit between predicted and target outputs when running the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_provider = CCPPDataProvider(\n", + " which_set='train',\n", + " input_dims=[0, 1],\n", + " batch_size=5000, \n", + " max_num_batches=1, \n", + " shuffle_order=False\n", + ")\n", + "\n", + "inputs, targets = data_provider.next()\n", + "\n", + "# Calculate predicted model outputs\n", + "outputs = model.fprop(inputs)[-1]\n", + "\n", + "# Plot target and predicted outputs against inputs on same axis\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n", + "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n", + "ax.set_xlabel('Input dim 1')\n", + "ax.set_ylabel('Input dim 2')\n", + "ax.set_zlabel('Output')\n", + "ax.legend(['Targets', 'Predictions'], frameon=False)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 6: visualising training trajectories in parameter space" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running the cell below will display an interactive widget which plots the trajectories of gradient-based training of the single-layer affine model on the CCPP dataset in the three dimensional parameter space (two weights plus bias) from random initialisations. Also shown on the right is a plot of the evolution of the error function (evaluated on the current batch) over training. By moving the sliders you can alter the training hyperparameters to investigate the effect they have on how training procedes.\n", + "\n", + "Some questions to explore:\n", + "\n", + " * Are there multiple local minima in parameter space here? Why?\n", + " * What happens to learning for very small learning rates? And very large learning rates?\n", + " * How does the batch size affect learning?\n", + " \n", + "**Note:** You don't need to understand how the code below works. The idea of this exercise is to help you understand the role of the various hyperparameters involved in gradient-descent based training methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from ipywidgets import interact\n", + "%matplotlib inline\n", + "\n", + "def setup_figure():\n", + " # create figure and axes\n", + " fig = plt.figure(figsize=(12, 6))\n", + " ax1 = fig.add_axes([0., 0., 0.5, 1.], projection='3d')\n", + " ax2 = fig.add_axes([0.6, 0.1, 0.4, 0.8])\n", + " # set axes properties\n", + " ax2.spines['right'].set_visible(False)\n", + " ax2.spines['top'].set_visible(False)\n", + " ax2.yaxis.set_ticks_position('left')\n", + " ax2.xaxis.set_ticks_position('bottom')\n", + " ax2.set_yscale('log')\n", + " ax1.set_xlim((-2, 2))\n", + " ax1.set_ylim((-2, 2))\n", + " ax1.set_zlim((-2, 2))\n", + " #set axes labels and title\n", + " ax1.set_title('Parameter trajectories over training')\n", + " ax1.set_xlabel('Weight 1')\n", + " ax1.set_ylabel('Weight 2')\n", + " ax1.set_zlabel('Bias')\n", + " ax2.set_title('Batch errors over training')\n", + " ax2.set_xlabel('Batch update number')\n", + " ax2.set_ylabel('Batch error')\n", + " return fig, ax1, ax2\n", + "\n", + "def visualise_training(n_epochs=1, batch_size=200, log_lr=-1., n_inits=5,\n", + " w_scale=1., b_scale=1., elev=30., azim=0.):\n", + " fig, ax1, ax2 = setup_figure()\n", + " # create seeded random number generator\n", + " rng = np.random.RandomState(1234)\n", + " # create data provider\n", + " data_provider = CCPPDataProvider(\n", + " input_dims=[0, 1],\n", + " batch_size=batch_size, \n", + " shuffle_order=False,\n", + " )\n", + " learning_rate = 10 ** log_lr\n", + " n_batches = data_provider.num_batches\n", + " weights_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1, 2))\n", + " biases_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1))\n", + " errors_traj = np.empty((n_inits, n_epochs * n_batches))\n", + " # randomly initialise parameters\n", + " weights = rng.uniform(-w_scale, w_scale, (n_inits, 1, 2))\n", + " biases = rng.uniform(-b_scale, b_scale, (n_inits, 1))\n", + " # store initial parameters\n", + " weights_traj[:, 0] = weights\n", + " biases_traj[:, 0] = biases\n", + " # iterate across different initialisations\n", + " for i in range(n_inits):\n", + " # iterate across epochs\n", + " for e in range(n_epochs):\n", + " # iterate across batches\n", + " for b, (inputs, targets) in enumerate(data_provider):\n", + " outputs = fprop(inputs, weights[i], biases[i])\n", + " errors_traj[i, e * n_batches + b] = error(outputs, targets)\n", + " grad_wrt_outputs = error_grad(outputs, targets)\n", + " weights_grad, biases_grad = grads_wrt_params(inputs, grad_wrt_outputs)\n", + " weights[i] -= learning_rate * weights_grad\n", + " biases[i] -= learning_rate * biases_grad\n", + " weights_traj[i, e * n_batches + b + 1] = weights[i]\n", + " biases_traj[i, e * n_batches + b + 1] = biases[i]\n", + " # choose a different color for each trajectory\n", + " colors = plt.cm.jet(np.linspace(0, 1, n_inits))\n", + " # plot all trajectories\n", + " for i in range(n_inits):\n", + " lines_1 = ax1.plot(\n", + " weights_traj[i, :, 0, 0], \n", + " weights_traj[i, :, 0, 1], \n", + " biases_traj[i, :, 0], \n", + " '-', c=colors[i], lw=2)\n", + " lines_2 = ax2.plot(\n", + " np.arange(n_batches * n_epochs),\n", + " errors_traj[i],\n", + " c=colors[i]\n", + " )\n", + " ax1.view_init(elev, azim)\n", + " plt.show()\n", + "\n", + "w = interact(\n", + " visualise_training,\n", + " elev=(-90, 90, 2),\n", + " azim=(-180, 180, 2), \n", + " n_epochs=(1, 5), \n", + " batch_size=(100, 1000, 100),\n", + " log_lr=(-3., 1.),\n", + " w_scale=(0., 2.),\n", + " b_scale=(0., 2.),\n", + " n_inits=(1, 10)\n", + ")\n", + "\n", + "for child in w.widget.children:\n", + " child.layout.width = '100%'" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 9690448e2e7fcec3eed5ff452eb84e405d43eb49 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Tue, 18 Sep 2018 03:18:18 +0100 Subject: [PATCH 02/22] Update getting-started-in-a-lab.md --- notes/getting-started-in-a-lab.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notes/getting-started-in-a-lab.md b/notes/getting-started-in-a-lab.md index 7f53851..b1c7a70 100644 --- a/notes/getting-started-in-a-lab.md +++ b/notes/getting-started-in-a-lab.md @@ -39,10 +39,10 @@ This should display a message indicate a new branch has been found and fetched, We now need to create and checkout a new local branch from the remote branch fetched above. This can be done by running ``` -git checkout -b lab[n] origin/mlp2017-8/lab[n] +git checkout -b lab[n] origin/mlp2018-9/lab[n] ``` -where again `lab[n]` corresponds to the relevant lab number fetched above e.g. `lab2`. This command creates a new local branch named `lab[n]` from the fetched branch on the remote repository `origin/mlp2017-8/lab[n]`. +where again `lab[n]` corresponds to the relevant lab number fetched above e.g. `lab2`. This command creates a new local branch named `lab[n]` from the fetched branch on the remote repository `origin/mlp2018-9/lab[n]`. Inside the `notebooks` directory there should new be a new notebook for today's lab. The notebook for the previous lab will now also have proposed solutions filled in. From 84e5822b756cfc5195c92fd1e3108b866aea9105 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Wed, 19 Sep 2018 18:50:22 +0100 Subject: [PATCH 03/22] Update environment-set-up.md --- notes/environment-set-up.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 99d8e83..8450f9d 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -246,7 +246,7 @@ This will change the code in the working directory to the current state of the c You should make sure you are on the first lab branch now by running: ``` -git checkout mlp2017-8/lab1 +git checkout mlp2018-9/lab1 ``` ## Installing the `mlp` Python package From cf322fbdd0e6cc2b725adfe2da81110363a04f50 Mon Sep 17 00:00:00 2001 From: Steve Renals Date: Thu, 20 Sep 2018 16:37:03 +0100 Subject: [PATCH 04/22] fixed typo --- notes/environment-set-up.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 8450f9d..a6c1353 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -404,7 +404,7 @@ Make sure we are on the first lab branch ``` cd ~/mlpractical -git checkout mlp2017-8/lab1 +git checkout mlp2018-9/lab1 ``` Install the `mlp` package in the environment in develop mode From e7ca50de606348a4dc45ff0bb54efd4b7d148323 Mon Sep 17 00:00:00 2001 From: Steve Renals Date: Thu, 20 Sep 2018 16:39:25 +0100 Subject: [PATCH 05/22] fixed typo --- notes/getting-started-in-a-lab.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/getting-started-in-a-lab.md b/notes/getting-started-in-a-lab.md index b1c7a70..fa0b324 100644 --- a/notes/getting-started-in-a-lab.md +++ b/notes/getting-started-in-a-lab.md @@ -34,7 +34,7 @@ We are now ready to fetch any updated code from the remote repository on Github. git fetch origin ``` -This should display a message indicate a new branch has been found and fetched, named `origin/mlp2017-8/lab[n]` where `[n]` is the relevant lab number e.g. `origin/mlp2017-8/lab2` for the second lab. +This should display a message indicate a new branch has been found and fetched, named `origin/mlp2018-9/lab[n]` where `[n]` is the relevant lab number e.g. `origin/mlp2018-9/lab2` for the second lab. We now need to create and checkout a new local branch from the remote branch fetched above. This can be done by running From aab2d7daa20bd22127eb022e852a2d9b50fe90aa Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 14:40:08 +0100 Subject: [PATCH 06/22] Update environment setup to reflect IAML and ANLP integration instructions --- notes/environment-set-up.md | 50 +++++++++++++++++++++++++++++++++++-- 1 file changed, 48 insertions(+), 2 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index a6c1353..68015c3 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -1,4 +1,4 @@ -# Environment set up +# 1. Environment set up *The instructions below are intentionally verbose as they try to explain the reasoning behind our choice of environment set up and to explain what each command we are asking you to run does. If you are already confident using bash, Conda environments and Git you may wish to instead use the much shorter [minimal set-up instructions](#minimal-set-up-instructions-for-dice) at the end which skip the explanations.* @@ -16,7 +16,40 @@ Conda can handle installation of the Python libraries we will be using and all t There are several options available for installing Conda on a system. Here we will use the Python 3 version of [Miniconda](http://conda.pydata.org/miniconda.html), which installs just Conda and its dependencies. An alternative is to install the [Anaconda Python distribution](https://docs.continuum.io/anaconda/), which installs Conda and a large selection of popular Python packages. As we will require only a small subset of these packages we will use the more barebones Miniconda to avoid eating into your DICE disk quota too much, however if installing on a personal machine you may wish to consider Anaconda if you want to explore other Python packages. -## Installing Miniconda +To proceed please choose either step 2 or 3. Do not execute both steps. Choose the one that best works with your course selections / machine setup. + +Choose step 2 if: + +1. You are taking ANLP or IAML in addition to the MLP course or +2. Are using a DICE machine and would rather skip manual installation of conda + +Choose step 3 if: + +1. You are NOT taking ANLP or IAML along with MLP and +1. You are using your own PC or laptop and want to do a manual conda installation or +2. You are curious and want to learn how to install miniconda on your own (highly recommended) or + +Again, choose 2 OR 3, NOT BOTH. + +## 2. Accessing conda via remote installation - SHOULD BE DONE BY ALL STUDENTS TAKING ANLP and IAML (along with MLP) courses + +Instead of having to install conda via miniconda from scratch, one can also access conda via a remote installation that we have made for all students. This is especially important for those taking ANLP and IAML. This step is vital if you want to be able to access the modules from those courses. + +``` +nano ~/.bashrc +``` +This opens the .bashrc file that is sourced whenever one starts a terminal. + +Append at the end +``` +source /group/teaching/conda/etc/profile.d/conda.sh +``` +This should be enough to enable conda and give you access to your packages. Please continue from the 'Creating the Conda environment section' if you have taken this path. + + + +## 3. Installing Miniconda From Scratch - Only do this step if you + We provide instructions here for getting an environment with all the required dependencies running on computers running the School of Informatics [DICE desktop](http://computing.help.inf.ed.ac.uk/dice-platform). The same instructions @@ -69,6 +102,19 @@ source ~/.benv From the next time you log in all future terminal sessions should have the updated `PATH` loaded by default. +To make sure that your conda installation will be available even from an ssh entry from a remote laptop please also do: + +``` +nano ~/.bashrc +``` +This opens the .bashrc file that is sourced whenever one starts a terminal. + +Append at the end + +``` +source ~/.benv +``` + ## Creating the Conda environment You should now have a working Conda installation. If you run From 6f46175efc01d57054e914526bd3af0332dd3fec Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 14:40:39 +0100 Subject: [PATCH 07/22] Update environment-set-up.md --- notes/environment-set-up.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 68015c3..346bd4f 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -48,7 +48,7 @@ This should be enough to enable conda and give you access to your packages. Plea -## 3. Installing Miniconda From Scratch - Only do this step if you +## 3. Installing Miniconda From Scratch - Only do this step if you have chosen not to do step 2 We provide instructions here for getting an environment with all the required dependencies running on computers running From 24ff3df48c1691542edec5b2c414e405301e3968 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 14:41:59 +0100 Subject: [PATCH 08/22] Emphasise installation pathways --- notes/environment-set-up.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 346bd4f..4f74b9e 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -16,6 +16,8 @@ Conda can handle installation of the Python libraries we will be using and all t There are several options available for installing Conda on a system. Here we will use the Python 3 version of [Miniconda](http://conda.pydata.org/miniconda.html), which installs just Conda and its dependencies. An alternative is to install the [Anaconda Python distribution](https://docs.continuum.io/anaconda/), which installs Conda and a large selection of popular Python packages. As we will require only a small subset of these packages we will use the more barebones Miniconda to avoid eating into your DICE disk quota too much, however if installing on a personal machine you may wish to consider Anaconda if you want to explore other Python packages. +# Choosing how to access conda: + To proceed please choose either step 2 or 3. Do not execute both steps. Choose the one that best works with your course selections / machine setup. Choose step 2 if: From dfedecc88929283ffc07042c5b86116d43ce1f06 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 14:43:29 +0100 Subject: [PATCH 09/22] Update environment-set-up.md --- notes/environment-set-up.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 4f74b9e..0b9f2a4 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -117,7 +117,7 @@ Append at the end source ~/.benv ``` -## Creating the Conda environment +## 4. Creating the Conda environment You should now have a working Conda installation. If you run @@ -168,7 +168,7 @@ conda clean -t These tarballs are usually cached to allow quicker installation into additional environments however we will only be using a single environment here so there is no need to keep them on disk. -## Getting the course code and a short introduction to Git +## 5. Getting the course code and a short introduction to Git The next step in getting our environment set up will be to download the course code. This is available in a Git repository on Github: @@ -297,7 +297,7 @@ You should make sure you are on the first lab branch now by running: git checkout mlp2018-9/lab1 ``` -## Installing the `mlp` Python package +## 6. Installing the `mlp` Python package In your local repository we noted above the presence of a `mlp` subdirectory. This contains the custom Python package implementing the NumPy based neural network framework we will be using in this course. @@ -339,7 +339,7 @@ python --- -## Adding a data directory variable to the environment +## 7. Adding a data directory variable to the environment We observed previously the presence of a `data` subdirectory in the local repository. This directory holds the data files that will be used in the course. To enable the data loaders in the `mlp` package to locate these data files we need to set a `MLP_DATA_DIR` environment variable pointing to this directory. @@ -367,7 +367,7 @@ mkdir .\etc\conda\deactivate.d set MLP_DATA_DIR=[path-to-local-repository]\data ``` -## Loading the first lab notebook +## 8. Loading the first lab notebook Your environment is now all set up so you can move on to the introductory exercises in the first lab notebook. From 9d92602f9056b05aa458cfdf6bdc0009e4d215ba Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 15:28:01 +0100 Subject: [PATCH 10/22] Update environment-set-up.md --- notes/environment-set-up.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 0b9f2a4..1e8c0a2 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -137,7 +137,7 @@ This bootstraps a new Conda environment named `mlp` with a minimal Python 3 inst We will now *activate* our created environment: ``` -source activate mlp +conda activate mlp ``` or on Windows only From 83ac38687faeed8f6de53d2c62cb1ae80a59f6d7 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 15:28:17 +0100 Subject: [PATCH 11/22] Update getting-started-in-a-lab.md --- notes/getting-started-in-a-lab.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/getting-started-in-a-lab.md b/notes/getting-started-in-a-lab.md index fa0b324..e952e87 100644 --- a/notes/getting-started-in-a-lab.md +++ b/notes/getting-started-in-a-lab.md @@ -7,7 +7,7 @@ Open a terminal window (`Applications > Terminal`). We first need to activate our `mlp` Conda environment: ``` -source activate mlp +conda activate mlp ``` We now need to fetch any new code for the lab from the Github repository and create a new branch for this lab's work. First change in to the `mlpractical` repoistory directory (if you cloned the repository to a different directory than the default you will need to adjust the command below accordingly): From a2235fdefdb0d781f928cb02d4427582f1cac1ca Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sun, 23 Sep 2018 15:30:58 +0100 Subject: [PATCH 12/22] Update environment-set-up.md --- notes/environment-set-up.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 1e8c0a2..e4a67a8 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -29,7 +29,7 @@ Choose step 3 if: 1. You are NOT taking ANLP or IAML along with MLP and 1. You are using your own PC or laptop and want to do a manual conda installation or -2. You are curious and want to learn how to install miniconda on your own (highly recommended) or +2. You are curious and want to learn how to install miniconda on your own (highly recommended) Again, choose 2 OR 3, NOT BOTH. From 3754af0382ac7e96028d3d58adfbcc1ff43b6185 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Mon, 24 Sep 2018 10:44:41 +0100 Subject: [PATCH 13/22] Update environment-set-up.md --- notes/environment-set-up.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index e4a67a8..ad66b94 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -148,7 +148,7 @@ activate mlp When a environment is activated its name will be prepended on to the prompt which should now look something like `(mlp) [machine-name]:~$` on DICE. -**You need to run this `source activate mlp` command every time you wish to activate the `mlp` environment in a terminal (for example at the beginning of each lab)**. When the environment is activated, the environment will be searched first when running commands so that e.g. `python` will launch the Python interpreter installed locally in the `mlp` environment rather than a system-wide version. +**You need to run this `conda activate mlp` command every time you wish to activate the `mlp` environment in a terminal (for example at the beginning of each lab)**. When the environment is activated, the environment will be searched first when running commands so that e.g. `python` will launch the Python interpreter installed locally in the `mlp` environment rather than a system-wide version. If you wish to deactivate an environment loaded in the current terminal e.g. to launch the system Python interpreter, you can run `source deactivate` (just `deactivate` on Windows). From 26b590bf89e15516049e56d0eacf93422f7337f5 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Mon, 24 Sep 2018 10:46:15 +0100 Subject: [PATCH 14/22] Sync with other environment.md --- notes/environment-set-up.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index ad66b94..77134fd 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -46,8 +46,14 @@ Append at the end ``` source /group/teaching/conda/etc/profile.d/conda.sh ``` -This should be enough to enable conda and give you access to your packages. Please continue from the 'Creating the Conda environment section' if you have taken this path. +This should be enough to enable conda and give you access to your packages every time to open a terminal. Please continue from the 'Creating the Conda environment section' if you have taken this path. +Also, do not forget to run + +``` +source ~/.bashrc +``` +In your current terminal session to get conda (automatic sourcing of .bashrc happens only when a new terminal is opened). ## 3. Installing Miniconda From Scratch - Only do this step if you have chosen not to do step 2 @@ -427,7 +433,7 @@ conda create -n mlp python=3 Activate our created environment: ``` -source activate mlp +conda activate mlp ``` Install the dependencies for the course into the new environment: From f6764174a5816b5adea4ee6d6a9ebcbdbf92ca35 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Mon, 24 Sep 2018 18:50:02 +0100 Subject: [PATCH 15/22] Fix shared conda issues --- notes/environment-set-up.md | 60 +++++++++++-------------------------- 1 file changed, 18 insertions(+), 42 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 77134fd..70a03b8 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -16,47 +16,8 @@ Conda can handle installation of the Python libraries we will be using and all t There are several options available for installing Conda on a system. Here we will use the Python 3 version of [Miniconda](http://conda.pydata.org/miniconda.html), which installs just Conda and its dependencies. An alternative is to install the [Anaconda Python distribution](https://docs.continuum.io/anaconda/), which installs Conda and a large selection of popular Python packages. As we will require only a small subset of these packages we will use the more barebones Miniconda to avoid eating into your DICE disk quota too much, however if installing on a personal machine you may wish to consider Anaconda if you want to explore other Python packages. -# Choosing how to access conda: -To proceed please choose either step 2 or 3. Do not execute both steps. Choose the one that best works with your course selections / machine setup. - -Choose step 2 if: - -1. You are taking ANLP or IAML in addition to the MLP course or -2. Are using a DICE machine and would rather skip manual installation of conda - -Choose step 3 if: - -1. You are NOT taking ANLP or IAML along with MLP and -1. You are using your own PC or laptop and want to do a manual conda installation or -2. You are curious and want to learn how to install miniconda on your own (highly recommended) - -Again, choose 2 OR 3, NOT BOTH. - -## 2. Accessing conda via remote installation - SHOULD BE DONE BY ALL STUDENTS TAKING ANLP and IAML (along with MLP) courses - -Instead of having to install conda via miniconda from scratch, one can also access conda via a remote installation that we have made for all students. This is especially important for those taking ANLP and IAML. This step is vital if you want to be able to access the modules from those courses. - -``` -nano ~/.bashrc -``` -This opens the .bashrc file that is sourced whenever one starts a terminal. - -Append at the end -``` -source /group/teaching/conda/etc/profile.d/conda.sh -``` -This should be enough to enable conda and give you access to your packages every time to open a terminal. Please continue from the 'Creating the Conda environment section' if you have taken this path. - -Also, do not forget to run - -``` -source ~/.bashrc -``` -In your current terminal session to get conda (automatic sourcing of .bashrc happens only when a new terminal is opened). - - -## 3. Installing Miniconda From Scratch - Only do this step if you have chosen not to do step 2 +## 2. Installing Miniconda We provide instructions here for getting an environment with all the required dependencies running on computers running @@ -123,7 +84,7 @@ Append at the end source ~/.benv ``` -## 4. Creating the Conda environment +## 3. Creating the Conda environment You should now have a working Conda installation. If you run @@ -174,7 +135,22 @@ conda clean -t These tarballs are usually cached to allow quicker installation into additional environments however we will only be using a single environment here so there is no need to keep them on disk. -## 5. Getting the course code and a short introduction to Git +***ANLP and IAML students only:*** +To have normal access to your ANLP and IAML environments please do the following: +1. ```nano .condarc``` +Add the following in the file: +2. +``` +envs_dirs: + - /group/teaching/conda/envs + +pkgs_dirs: + - /group/teaching/conda/pkgs + - ~/miniconda3/pkgs +``` +3. Exit by using control + x and then choosing 'yes' at the exit prompt. + +## 4. Getting the course code and a short introduction to Git The next step in getting our environment set up will be to download the course code. This is available in a Git repository on Github: From 2f0729b4592bfc753cb6bb50ab095938f99a8f10 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Mon, 24 Sep 2018 18:51:45 +0100 Subject: [PATCH 16/22] Update environment-set-up.md --- notes/environment-set-up.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 70a03b8..25e2bed 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -138,8 +138,7 @@ These tarballs are usually cached to allow quicker installation into additional ***ANLP and IAML students only:*** To have normal access to your ANLP and IAML environments please do the following: 1. ```nano .condarc``` -Add the following in the file: -2. +2. Add the following lines in the file: ``` envs_dirs: - /group/teaching/conda/envs From 603cdc5ebe5848497f9b9db2eff4cc0d15f52200 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Tue, 25 Sep 2018 12:29:08 +0100 Subject: [PATCH 17/22] Update environment-set-up.md --- notes/environment-set-up.md | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 25e2bed..94336fe 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -58,7 +58,9 @@ definition in `.bashrc`. As the DICE bash start-up mechanism differs from the st On DICE, append the Miniconda binaries directory to `PATH` in manually in `~/.benv` using ``` -echo "export PATH=\""\$PATH":$HOME/miniconda3/bin\"" >> ~/.benv +echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc +echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.benv + ``` For those who this appears a bit opaque to and want to know what is going on see here [1](#f1). @@ -69,20 +71,12 @@ We now need to `source` the updated `~/.benv` so that the `PATH` variable in the source ~/.benv ``` -From the next time you log in all future terminal sessions should have the updated `PATH` loaded by default. - -To make sure that your conda installation will be available even from an ssh entry from a remote laptop please also do: +From the next time you log in all future terminal sessions should have conda readily available via: ``` -nano ~/.bashrc +conda activate ``` -This opens the .bashrc file that is sourced whenever one starts a terminal. - -Append at the end -``` -source ~/.benv -``` ## 3. Creating the Conda environment From 5f37710e6d02aefc523d84b28b97dfdf6fd80841 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Tue, 25 Sep 2018 12:36:35 +0100 Subject: [PATCH 18/22] Update environment-set-up.md --- notes/environment-set-up.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 94336fe..cce7b2e 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -60,7 +60,6 @@ On DICE, append the Miniconda binaries directory to `PATH` in manually in `~/.be ``` echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.benv - ``` For those who this appears a bit opaque to and want to know what is going on see here [1](#f1). @@ -384,7 +383,8 @@ You will then be asked whether to prepend the Miniconda binaries directory to th Append the Miniconda binaries directory to `PATH` in manually in `~/.benv`: ``` -echo "export PATH=\""\$PATH":$HOME/miniconda3/bin\"" >> ~/.benv +echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc +echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.benv ``` `source` the updated `~/.benv`: From c1e3b878f1b6322527842d78fdd1ab8714529cd5 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Tue, 25 Sep 2018 15:22:31 +0100 Subject: [PATCH 19/22] Automate the setting of user id --- notes/environment-set-up.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index cce7b2e..7725c9d 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -58,8 +58,8 @@ definition in `.bashrc`. As the DICE bash start-up mechanism differs from the st On DICE, append the Miniconda binaries directory to `PATH` in manually in `~/.benv` using ``` -echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc -echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.benv +echo ". /afs/inf.ed.ac.uk/user/${USER:0:3}/$USER/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc +echo ". /afs/inf.ed.ac.uk/user/${USER:0:3}/$USER/miniconda3/etc/profile.d/conda.sh" >> ~/.benv ``` For those who this appears a bit opaque to and want to know what is going on see here [1](#f1). From e04620c9b62ed0b8bb4e2b565cdb96aaf2e53078 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Tue, 25 Sep 2018 15:46:47 +0100 Subject: [PATCH 20/22] Update environment-set-up.md --- notes/environment-set-up.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notes/environment-set-up.md b/notes/environment-set-up.md index 7725c9d..0144f20 100644 --- a/notes/environment-set-up.md +++ b/notes/environment-set-up.md @@ -383,8 +383,8 @@ You will then be asked whether to prepend the Miniconda binaries directory to th Append the Miniconda binaries directory to `PATH` in manually in `~/.benv`: ``` -echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc -echo ". /afs/inf.ed.ac.uk/user/s14/s1473470/miniconda3/etc/profile.d/conda.sh" >> ~/.benv +echo ". /afs/inf.ed.ac.uk/user/${USER:0:3}/$USER/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc +echo ". /afs/inf.ed.ac.uk/user/${USER:0:3}/$USER/miniconda3/etc/profile.d/conda.sh" >> ~/.benv ``` `source` the updated `~/.benv`: From 4367faf20628f5127dfd76b6a184fe250d613656 Mon Sep 17 00:00:00 2001 From: Antreas Antoniou Date: Sat, 29 Sep 2018 21:37:12 +0100 Subject: [PATCH 21/22] Add lab3 --- .gitsync | 1 + mlp/data_providers.py | 83 +- mlp/errors.py | 142 +- mlp/layers.py | 130 +- mlp/models.py | 74 +- mlp/optimisers.py | 5 +- notebooks/01_Introduction.ipynb | 156 +- notebooks/02_Single_layer_models.ipynb | 2538 ++++++++++++++++++- notebooks/03_Multiple_layer_models.ipynb | 833 ++++++ notebooks/res/fprop-bprop-block-diagram.pdf | Bin 0 -> 13789 bytes notebooks/res/fprop-bprop-block-diagram.png | Bin 0 -> 7112 bytes notebooks/res/fprop-bprop-block-diagram.tex | 65 + 12 files changed, 3861 insertions(+), 166 deletions(-) create mode 100644 .gitsync create mode 100644 notebooks/03_Multiple_layer_models.ipynb create mode 100644 notebooks/res/fprop-bprop-block-diagram.pdf create mode 100644 notebooks/res/fprop-bprop-block-diagram.png create mode 100644 notebooks/res/fprop-bprop-block-diagram.tex diff --git a/.gitsync b/.gitsync new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/.gitsync @@ -0,0 +1 @@ + diff --git a/mlp/data_providers.py b/mlp/data_providers.py index ebea079..c20063b 100644 --- a/mlp/data_providers.py +++ b/mlp/data_providers.py @@ -35,23 +35,54 @@ def __init__(self, inputs, targets, batch_size, max_num_batches=-1, """ self.inputs = inputs self.targets = targets - self.batch_size = batch_size - assert max_num_batches != 0 and not max_num_batches < -1, ( - 'max_num_batches should be -1 or > 0') - self.max_num_batches = max_num_batches + if batch_size < 1: + raise ValueError('batch_size must be >= 1') + self._batch_size = batch_size + if max_num_batches == 0 or max_num_batches < -1: + raise ValueError('max_num_batches must be -1 or > 0') + self._max_num_batches = max_num_batches + self._update_num_batches() + self.shuffle_order = shuffle_order + self._current_order = np.arange(inputs.shape[0]) + if rng is None: + rng = np.random.RandomState(DEFAULT_SEED) + self.rng = rng + self.new_epoch() + + @property + def batch_size(self): + """Number of data points to include in each batch.""" + return self._batch_size + + @batch_size.setter + def batch_size(self, value): + if value < 1: + raise ValueError('batch_size must be >= 1') + self._batch_size = value + self._update_num_batches() + + @property + def max_num_batches(self): + """Maximum number of batches to iterate over in an epoch.""" + return self._max_num_batches + + @max_num_batches.setter + def max_num_batches(self, value): + if value == 0 or value < -1: + raise ValueError('max_num_batches must be -1 or > 0') + self._max_num_batches = value + self._update_num_batches() + + def _update_num_batches(self): + """Updates number of batches to iterate over.""" # maximum possible number of batches is equal to number of whole times # batch_size divides in to the number of data points which can be # found using integer division - possible_num_batches = self.inputs.shape[0] // batch_size + possible_num_batches = self.inputs.shape[0] // self.batch_size if self.max_num_batches == -1: self.num_batches = possible_num_batches else: self.num_batches = min(self.max_num_batches, possible_num_batches) - self.shuffle_order = shuffle_order - if rng is None: - rng = np.random.RandomState(DEFAULT_SEED) - self.rng = rng - self.reset() def __iter__(self): """Implements Python iterator interface. @@ -63,27 +94,36 @@ def __iter__(self): """ return self - def reset(self): - """Resets the provider to the initial state to use in a new epoch.""" + def new_epoch(self): + """Starts a new epoch (pass through data), possibly shuffling first.""" self._curr_batch = 0 if self.shuffle_order: self.shuffle() - def shuffle(self): - """Randomly shuffles order of data.""" - new_order = self.rng.permutation(self.inputs.shape[0]) - self.inputs = self.inputs[new_order] - self.targets = self.targets[new_order] - def __next__(self): return self.next() + def reset(self): + """Resets the provider to the initial state.""" + inv_perm = np.argsort(self._current_order) + self._current_order = self._current_order[inv_perm] + self.inputs = self.inputs[inv_perm] + self.targets = self.targets[inv_perm] + self.new_epoch() + + def shuffle(self): + """Randomly shuffles order of data.""" + perm = self.rng.permutation(self.inputs.shape[0]) + self._current_order = self._current_order[perm] + self.inputs = self.inputs[perm] + self.targets = self.targets[perm] + def next(self): """Returns next data batch or raises `StopIteration` if at end.""" if self._curr_batch + 1 > self.num_batches: - # no more batches in current iteration through data set so reset - # the dataset for another pass and indicate iteration is at end - self.reset() + # no more batches in current iteration through data set so start + # new epoch ready for another pass and indicate iteration is at end + self.new_epoch() raise StopIteration() # create an index slice corresponding to current batch number batch_slice = slice(self._curr_batch * self.batch_size, @@ -93,7 +133,6 @@ def next(self): self._curr_batch += 1 return inputs_batch, targets_batch - class MNISTDataProvider(DataProvider): """Data provider for MNIST handwritten digit images.""" diff --git a/mlp/errors.py b/mlp/errors.py index 712fe59..5ef95f7 100644 --- a/mlp/errors.py +++ b/mlp/errors.py @@ -15,6 +15,135 @@ class SumOfSquaredDiffsError(object): """Sum of squared differences (squared Euclidean distance) error.""" + def __call__(self, outputs, targets): + """Calculates error function given a batch of outputs and targets. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Scalar cost function value. + """ + return 0.5 * np.mean(np.sum((outputs - targets)**2, axis=1)) + + def grad(self, outputs, targets): + """Calculates gradient of error function with respect to outputs. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Gradient of error function with respect to outputs. + """ + return (outputs - targets) / outputs.shape[0] + + def __repr__(self): + return 'MeanSquaredErrorCost' + + +class BinaryCrossEntropyError(object): + """Binary cross entropy error.""" + + def __call__(self, outputs, targets): + """Calculates error function given a batch of outputs and targets. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Scalar error function value. + """ + return -np.mean( + targets * np.log(outputs) + (1. - targets) * np.log(1. - ouputs)) + + def grad(self, outputs, targets): + """Calculates gradient of error function with respect to outputs. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Gradient of error function with respect to outputs. + """ + return ((1. - targets) / (1. - outputs) - + (targets / outputs)) / outputs.shape[0] + + def __repr__(self): + return 'BinaryCrossEntropyError' + + +class BinaryCrossEntropySigmoidError(object): + """Binary cross entropy error with logistic sigmoid applied to outputs.""" + + def __call__(self, outputs, targets): + """Calculates error function given a batch of outputs and targets. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Scalar error function value. + """ + probs = 1. / (1. + np.exp(-outputs)) + return -np.mean( + targets * np.log(probs) + (1. - targets) * np.log(1. - probs)) + + def grad(self, outputs, targets): + """Calculates gradient of error function with respect to outputs. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Gradient of error function with respect to outputs. + """ + probs = 1. / (1. + np.exp(-outputs)) + return (probs - targets) / outputs.shape[0] + + def __repr__(self): + return 'BinaryCrossEntropySigmoidError' + + +class CrossEntropyError(object): + """Multi-class cross entropy error.""" + + def __call__(self, outputs, targets): + """Calculates error function given a batch of outputs and targets. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Scalar error function value. + """ + return -np.mean(np.sum(targets * np.log(outputs), axis=1)) + + def grad(self, outputs, targets): + """Calculates gradient of error function with respect to outputs. + + Args: + outputs: Array of model outputs of shape (batch_size, output_dim). + targets: Array of target outputs of shape (batch_size, output_dim). + + Returns: + Gradient of error function with respect to outputs. + """ + return -(targets / outputs) / outputs.shape[0] + + def __repr__(self): + return 'CrossEntropyError' + + +class CrossEntropySoftmaxError(object): + """Multi-class cross entropy error with Softmax applied to outputs.""" + def __call__(self, outputs, targets): """Calculates error function given a batch of outputs and targets. @@ -25,7 +154,9 @@ def __call__(self, outputs, targets): Returns: Scalar error function value. """ - raise NotImplementedError() + probs = np.exp(outputs) + probs /= probs.sum(-1)[:, None] + return -np.mean(np.sum(targets * np.log(probs), axis=1)) def grad(self, outputs, targets): """Calculates gradient of error function with respect to outputs. @@ -35,10 +166,11 @@ def grad(self, outputs, targets): targets: Array of target outputs of shape (batch_size, output_dim). Returns: - Gradient of error function with respect to outputs. This should be - an array of shape (batch_size, output_dim). + Gradient of error function with respect to outputs. """ - raise NotImplementedError() + probs = np.exp(outputs) + probs /= probs.sum(-1)[:, None] + return (probs - targets) / outputs.shape[0] def __repr__(self): - return 'SumOfSquaredDiffsError' + return 'CrossEntropySoftmaxError' diff --git a/mlp/layers.py b/mlp/layers.py index e2e871b..cc4cdda 100644 --- a/mlp/layers.py +++ b/mlp/layers.py @@ -73,7 +73,18 @@ def params(self): """Returns a list of parameters of layer. Returns: - List of current parameter values. + List of current parameter values. This list should be in the + corresponding order to the `values` argument to `set_params`. + """ + raise NotImplementedError() + + @params.setter + def params(self, values): + """Sets layer parameters from a list of values. + + Args: + values: List of values to set parameters to. This list should be + in the corresponding order to what is returned by `get_params`. """ raise NotImplementedError() @@ -86,8 +97,7 @@ class AffineLayer(LayerWithParameters): def __init__(self, input_dim, output_dim, weights_initialiser=init.UniformInit(-0.1, 0.1), - biases_initialiser=init.ConstantInit(0.), - weights_cost=None, biases_cost=None): + biases_initialiser=init.ConstantInit(0.)): """Initialises a parameterised affine layer. Args: @@ -113,7 +123,26 @@ def fprop(self, inputs): Returns: outputs: Array of layer outputs of shape (batch_size, output_dim). """ - raise NotImplementedError() + return inputs.dot(self.weights.T) + self.biases + + def bprop(self, inputs, outputs, grads_wrt_outputs): + """Back propagates gradients through a layer. + + Given gradients with respect to the outputs of the layer calculates the + gradients with respect to the layer inputs. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + outputs: Array of layer outputs calculated in forward pass of + shape (batch_size, output_dim). + grads_wrt_outputs: Array of gradients with respect to the layer + outputs of shape (batch_size, output_dim). + + Returns: + Array of gradients with respect to the layer inputs of shape + (batch_size, input_dim). + """ + return grads_wrt_outputs.dot(self.weights) def grads_wrt_params(self, inputs, grads_wrt_outputs): """Calculates gradients with respect to layer parameters. @@ -127,13 +156,104 @@ def grads_wrt_params(self, inputs, grads_wrt_outputs): list of arrays of gradients with respect to the layer parameters `[grads_wrt_weights, grads_wrt_biases]`. """ - raise NotImplementedError() + + grads_wrt_weights = np.dot(grads_wrt_outputs.T, inputs) + grads_wrt_biases = np.sum(grads_wrt_outputs, axis=0) + return [grads_wrt_weights, grads_wrt_biases] @property def params(self): """A list of layer parameter values: `[weights, biases]`.""" return [self.weights, self.biases] + @params.setter + def params(self, values): + self.weights = values[0] + self.biases = values[1] + def __repr__(self): return 'AffineLayer(input_dim={0}, output_dim={1})'.format( self.input_dim, self.output_dim) + + +class SigmoidLayer(Layer): + """Layer implementing an element-wise logistic sigmoid transformation.""" + + def fprop(self, inputs): + """Forward propagates activations through the layer transformation. + + For inputs `x` and outputs `y` this corresponds to + `y = 1 / (1 + exp(-x))`. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + + Returns: + outputs: Array of layer outputs of shape (batch_size, output_dim). + """ + return 1. / (1. + np.exp(-inputs)) + + def bprop(self, inputs, outputs, grads_wrt_outputs): + """Back propagates gradients through a layer. + + Given gradients with respect to the outputs of the layer calculates the + gradients with respect to the layer inputs. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + outputs: Array of layer outputs calculated in forward pass of + shape (batch_size, output_dim). + grads_wrt_outputs: Array of gradients with respect to the layer + outputs of shape (batch_size, output_dim). + + Returns: + Array of gradients with respect to the layer inputs of shape + (batch_size, input_dim). + """ + return grads_wrt_outputs * outputs * (1. - outputs) + + def __repr__(self): + return 'SigmoidLayer' + + +class SoftmaxLayer(Layer): + """Layer implementing a softmax transformation.""" + + def fprop(self, inputs): + """Forward propagates activations through the layer transformation. + + For inputs `x` and outputs `y` this corresponds to + + `y = exp(x) / sum(exp(x))`. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + + Returns: + outputs: Array of layer outputs of shape (batch_size, output_dim). + """ + exp_inputs = np.exp(inputs) + return exp_inputs / exp_inputs.sum(-1)[:, None] + + def bprop(self, inputs, outputs, grads_wrt_outputs): + """Back propagates gradients through a layer. + + Given gradients with respect to the outputs of the layer calculates the + gradients with respect to the layer inputs. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + outputs: Array of layer outputs calculated in forward pass of + shape (batch_size, output_dim). + grads_wrt_outputs: Array of gradients with respect to the layer + outputs of shape (batch_size, output_dim). + + Returns: + Array of gradients with respect to the layer inputs of shape + (batch_size, input_dim). + """ + return (outputs * (grads_wrt_outputs - + (grads_wrt_outputs * outputs).sum(-1)[:, None])) + + def __repr__(self): + return 'SoftmaxLayer' diff --git a/mlp/models.py b/mlp/models.py index 86a0472..5e35dbc 100644 --- a/mlp/models.py +++ b/mlp/models.py @@ -59,9 +59,75 @@ def grads_wrt_params(self, activations, grads_wrt_outputs): """ return self.layer.grads_wrt_params(activations[0], grads_wrt_outputs) - def params_cost(self): - """Calculates the parameter dependent cost term of the model.""" - return self.layer.params_cost() - def __repr__(self): return 'SingleLayerModel(' + str(layer) + ')' + + +class MultipleLayerModel(object): + """A model consisting of multiple layers applied sequentially.""" + + def __init__(self, layers): + """Create a new multiple layer model instance. + + Args: + layers: List of the the layer objecst defining the model in the + order they should be applied from inputs to outputs. + """ + self.layers = layers + + @property + def params(self): + """A list of all of the parameters of the model.""" + params = [] + for layer in self.layers: + if isinstance(layer, LayerWithParameters): + params += layer.params + return params + + def fprop(self, inputs): + """Forward propagates a batch of inputs through the model. + + Args: + inputs: Batch of inputs to the model. + + Returns: + List of the activations at the output of all layers of the model + plus the inputs (to the first layer) as the first element. The + last element of the list corresponds to the model outputs. + """ + activations = [inputs] + for i, layer in enumerate(self.layers): + activations.append(self.layers[i].fprop(activations[i])) + return activations + + def grads_wrt_params(self, activations, grads_wrt_outputs): + """Calculates gradients with respect to the model parameters. + + Args: + activations: List of all activations from forward pass through + model using `fprop`. + grads_wrt_outputs: Gradient with respect to the model outputs of + the scalar function parameter gradients are being calculated + for. + + Returns: + List of gradients of the scalar function with respect to all model + parameters. + """ + grads_wrt_params = [] + for i, layer in enumerate(self.layers[::-1]): + inputs = activations[-i - 2] + outputs = activations[-i - 1] + grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs) + if isinstance(layer, LayerWithParameters): + grads_wrt_params += layer.grads_wrt_params( + inputs, grads_wrt_outputs)[::-1] + grads_wrt_outputs = grads_wrt_inputs + return grads_wrt_params[::-1] + + def __repr__(self): + return ( + 'MultiLayerModel(\n ' + + '\n '.join([str(layer) for layer in self.layers]) + + '\n)' + ) diff --git a/mlp/optimisers.py b/mlp/optimisers.py index 01dd8b6..8222807 100644 --- a/mlp/optimisers.py +++ b/mlp/optimisers.py @@ -121,6 +121,7 @@ def train(self, num_epochs, stats_interval=5): and the second being a dict mapping the labels for the statistics recorded to their column index in the array. """ + start_train_time = time.clock() run_stats = [list(self.get_epoch_stats().values())] for epoch in range(1, num_epochs + 1): start_time = time.clock() @@ -130,5 +131,7 @@ def train(self, num_epochs, stats_interval=5): stats = self.get_epoch_stats() self.log_stats(epoch, epoch_time, stats) run_stats.append(list(stats.values())) - return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())} + finish_train_time = time.clock() + total_train_time = finish_train_time - start_train_time + return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())}, total_train_time diff --git a/notebooks/01_Introduction.ipynb b/notebooks/01_Introduction.ipynb index 0fcf77b..a25d342 100644 --- a/notebooks/01_Introduction.ipynb +++ b/notebooks/01_Introduction.ipynb @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "nbpresent": { "id": "2bced39d-ae3a-4603-ac94-fbb6a6283a96" @@ -240,14 +240,12 @@ "outputs": [ { "data": { - "image/png": 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VO55J6AQka633aK0LgenAmXqBDAWmuSSZu6nXHm5bbCwxemI7fNYLTuw84yHZBcWMnLSa7zYeIcjXi8m3dpICwQRKKe7vGcdL/VtjtSjeX7Kb+2ZuoMgmIx+EcEtpe42fsUc3Gn3ERi6q8gUCuGeREA0cLHE/xfHYvyilGgKNgV9KPOynlFqtlFqulLq28mK6iZoN4daFUP9CyDwEn/eGw+tK3TU1u4AbPlnO78knCQ/yZfqoC7moqcx/YKahnRow8eYOBPhYmbPuEKO/XEN+kc3sWEKIko5tgc96GxMkRcXDyJ8hvJnZqVxCudvc8kqpgUAvrfVtjvs3Ap201neXsu/DQL2Szyml6mqtDyulmmAUD1dorXeXcuwoYBRARERE+5kzZ1bOF+QiFlsBLbe8Qq20NRRb/dnc6nHSa7b++/mTeXZeX5XP0VxNhL/iwY5+RAacvUbMzs4mKCioMqN7rIpsm70ZNt5YnU92ETQPszC2nR/+Xp576UE+N2WTtimbO7ZNcMZ2Wm96Du/iHE6FtmJzq8exeQW4PEdFt023bt3WaK07nG0/dywSugDPaK17Oe4/CqC1fqmUfdcBd2mtl5XxWpOA77TWs870nnFxcXrHjh3nG918xYXGctObZ4PV11gH4oKr2J+aww2frOBQeh4tooKZdGtHImv4leslk5KSSExMrNTYnqqi22bnsSyGT1zB8awC4uuHMumWjoQGeOZEVvK5KZu0Tdncrm2SFxsz3RblQtxVMOBz8C7fz86KVtFto5QqV5HgjpcbVgExSqnGSikfYAjwr1EKSqk4oCbwZ4nHaiqlfB3b4cDFwNbTj62yvHyg/yfQYSTYCmDmjRxdPoPBHy/nUHoe7RvWZPodF5a7QBCuFVu7Bl+P7kK9mv6sP5jOkAnLOZ6Vb3YsIaqnnT/BtCFGgdD2Bhj0pWkFgpncrkjQWhcDY4CFwDZgptZ6i1LqOaVU3xK7DgWm6/8/FdIcWK2U2gAsAV4uOSqiWrBY4eo34OJxYC8m/MfRtMtOolPjMCbf2olgP2+zE4ozaFgrkK9Hd6FpRCDbj2YxVAoFIVxvx48wYxjYCqHj7dDvfbC63WBAl3C7IgFAa/2D1jpWa91Ua/2C47GntNbzSuzzjNb6kdOOW6a1bq21buv491NXZ3cLSrG91X18qvrjhZ13fd7jy84pBPlWzw+5p4kK8WfGHV24oE4Ndp8wLhXJNM5CuMj2H4xLDLZC6DwarnoNLG75q9Ilqu9XXoXtOJrF0E9W8Hze9cwNHoYVO75zR8GmM3bNEG4kPMiXr27rTFztGiQfz+aGT5ZzMlsKBSEq1bbvYOZNYC+CC++E3i9X2UmSykuKhCpm94lshk1czqncIrrFRdJrzHi47BFj6tBvbjc6NQqPUCvIl69u70xs7SB2OQqFVCkUhKgcOxbA1zcbBUKXMdDrxWpfIIAUCVXKgdRchn2ygpPZhVwSE86Hw9vj5+MF3R415hbXdvhmlPHNIDxCeJAvU2+/kJjIIHYey+aGT1ZwKqfQ7FhCVC27lzjOIBTDRXdDz/9KgeAgRUIVcSg9j6GfLOdoZj6dGocx4cYO+Hlb/9nhsof/7szIzJuNqZyFR/irUGgWGcSOY1mOBbnOfVEvIUQJB5bD9BuMPgidRhkL6EmB8DcpEqqA45n5DPvEGOaY0CCUz0Z0xN/H+v87KQXdnzF66toKYNpQ45tDeISIGkYfhfph/mxMyeC2yatlZkYhztfhdcZaDEW5ED8Mer8iBcJppEjwcBm5Rdz46Ur2pebSKjqYSbd0KnsUg1Jw5avGN0NRrvHNcXi9awOLc1Y72I+vRl5IZA1fVuxN466v1spaD0Kcq+Pb4Mv+UJAJLa6Fvu9W61EMZZEW8WB5hTZGTl7FjmNZNIsM4otbOxPif5Z5ECwWuGY8tOhnfHNM6Q8nk10TWJy3BrUCmHJbZ2oGeLN4+3Hum7kBm929Zk0Vwu2d2g9fXAt5aRDTy5iEzmI9+3HVkBQJHqrIZueuqWtZvf8UdUP8+OLWToQFlnMKX6sX9J8IzbpDbipMuc5YAlV4hNjaNZh8q3HGaP6Gwzw9bzPuNr26EG4rJ9X44yj7KDTsCoMmG7PVilJJkeCBtNY8MnsTv2w/Ts0Ab74Y2Zm6of7OvYiXDwz6AqLbQ/oBmDIA8jMqJ7CocG3qhTLx5g74eFmYsvwA7y+Rs0FCnFVhDkwdCKnJULsVDJ0K3k7+7KxmpEjwQC8v2M7stSkE+Fj5/JZONIs8x5XBfALhhq+hVjM4tgmmD4MimQLYU1zYpBbjh8SjFLz+006+Xn3w7AcJUV3ZioyRXYfWQEgDGDYL/ELMTuX2pEjwMJ//sZePl+7B26r4aHh74uuHnt8LBtaC4d9AUB3Y9xvMGQV26TXvKXq3iuKZa1oC8Mg3m1iy47jJiYRwQ1rDvLsh+WfwD4Mbv4HgKLNTeQQpEjzIwi1Hee47Y72q1wa05dLYiIp54ZoNYfhs8A2GrXNh4WMV87rCJW6+qBH/SWyKza65c8paNhxMNzuSEO7ll+dhwzTwDjDOIITHmJ3IY0iR4CHWHTjF2Gnr0Boe6BnLtQnRFfsGdVrB0Glg8YYVH8Hyjyr29UWleqhXHP0ToskrsnHrpFUcSM01O5IQ7mHtl/DbG6CsRj+seu3NTuRRpEjwAPtTc7ht8moKiu0M6Vifu7o1q5w3atTVWBIV4MdHjNXQhEdQSvHKgDZcEhNOak4ht0xaSUaezMooqrndS+C7ccb21a9DTA9z83ggKRLc3KmcQkZ8vorUnEIujY3g+WtboSpzRrC2gyHxMUDD7JEEZUmveU/hbbXw/rB2xNYOYveJHO78ao1MtiSqr2NbS6zHMBY63Gp2Io8kRYIbKyy2c8eUNew9mUPzqGA+GNYOb6sL/ssuewja3gBFubTe9F9jiKTwCMF+3nw2oiPhQb78kZzKE3NkDgVRDWUdhamDHLMp9oPuz5qdyGO5ZZGglOqtlNqhlEpWSj1SyvMjlFInlFLrHbfbSjx3s1Jql+N2s2uTVxytNY/P2cTKvWnUDvbl8xEdy55uuaIpBde8A40uwbfwFEwdDPmZrnlvcd7q1Qxg4s0d8PWyMGP1QT76dY/ZkYRwncJcmDYEMg5CvY5w3ccy3fJ5cLuWU0pZgfeBK4EWwFClVItSdp2htY533CY6jg0DngY6A52Ap5VSNV0UvUJ98tsevl6Tgp+3hYk3daROiJ9rA3j5wOAvyQmoB8e3wje3y9BIDxJfP5S3BscD8MqP21mw6YjJiYRwAa1h7l3Gwk2hDWHINJks6Ty5XZGA8cs9WWu9R2tdCEwH+pXz2F7Az1rrNK31KeBnoHcl5aw0i7Ye46UF2wF4c1A8reuZNOGHf002t3oC/EJh54+wWE7ZeZKrWkfxcO8LALh35no2H5IZNUUVt/Q12PIN+NSAG2ZAUAUNE6/G3LFIiAZKTh2X4njsdNcrpTYqpWYppeo7eazb2nYkk3um/zPU8arW5k74kRcQZQwbsnjBH+/A+qmm5hHOGX1ZE65vV4/8IjujvljNiawCsyMJUTm2zoUlLwAKBnwKkc3NTlQluOgit1NK67p/es+r+cA0rXWBUmo0MBm4vJzHGm+i1ChgFEBERARJSUnnHLiiZBZonv0zj5xCTZcoKy1VCklJh0zNlJ2dTdKBIOo2vY3YXR9hn3s36/dnkBki34DZ2dlu8bk5m161NOtDLOzOyGfo+7/wcCc/vC2VOEIGz2kbM0jblO1c2yYoaw8J6x7GCuxucjMHD/vCYedfx52Z9blxxyIhBahf4n494HDJHbTWqSXufgK8UuLYxNOOTSrtTbTWE4AJAHFxcToxMbG03VymyGZn2MQVpObnktAglM9vvxA/b/OXLk1KSsJom0T43oZl1Se02/kG3L4EQuuf5eiq7Z+2cX/xnfLp994fJKfn81NqGK8OaFOpQ2k9qW1cTdqmbOfUNlnH4JM7wV4I8cNp2u9tmlbmMHGTmPW5ccfLDauAGKVUY6WUDzAEmFdyB6VUyXPwfYFtju2FQE+lVE1Hh8Wejsfc3nPzt/49kuHj4e3dokD4l94vQ5NEyDkBM4ZBUZ7ZiUQ5Rdbw45ObOuDnbeHrNSl89sc+syMJcf6KC425EDIPQf0Loc+bxugsUWHcrkjQWhcDYzB+uW8DZmqttyilnlNK9XXsNlYptUUptQEYC4xwHJsGPI9RaKwCnnM85tamrzzAl8v342O18NHw9kQGu3gkQ3lZvWDA51CzERzZAPPvMXoTC4/QKjqE1we2BeCF77fy+66TJicS4jz9+DAcXA7B0TD4S/DyNTtRleN2RQKA1voHrXWs1rqp1voFx2NPaa3nObYf1Vq31Fq31Vp301pvL3HsZ1rrZo7b52Z9DeW1Zn8aT87dDMAL17UioYGbj9gMCIMhU8E7EDbOgOUfmJ1IOKFPm7rcfXkz7BrGTFvLwTRZ40F4qDWTYPVnYPWFwVMgKNLsRFWSWxYJ1cXRjHxGT1lLkU0z4qJGDOzgIdf4a7eEax3FwU9Pwp4kU+MI54zrHku3uAjSc4u448s15BXK/BfCwxxYAd8/YGxf8zZEtzM3TxUmRYJJCoptjJ6yhhNZBXRpUovHr/aw0QItr4VL7gdtg69vgVP7zU4kyslqUbw9JIFGtQLYeiSTR7/ZKFM3C8+ReQRm3gj2Iug8GuJvMDtRlSZFgkmem7+V9QfTiQ71531XrclQ0bo9Ds16QF6a0ZGxUE5de4oQf28+vrEDAT5Wvl1/mM+lI6PwBH91VMw+Bg27Qs//mp2oyvPA30ye7+vVB/lqxQF8vCx8OLwdYYE+Zkc6NxYrXD8RwprA0U3w/X3SkdGDxNWp8U9Hxh+28efu1LMcIYTJFj4GKSshuB4MnARWb7MTVXlSJLjY5kMZPP6t0VHx+X4taVMv1ORE58k/1Og05OUPG6YZHYmEx7iqdRSjL2uKza65e9pajmbkmx1JiNJtmAGrPgGrjzELrEy57BJSJLjQqZxCRk9ZQ2GxnaGd6jO4YwOzI1WM2i2h73hje8HDkLLa3DzCKQ/2iuPiZrU4mV3IXVPXUlhsNzuSEP/v6GZjyDXAla9Cvfbm5qlGpEhwEZtdc8+M9aScyqNtvRCe6dvS7EgVq80g6DTK6Ew08ybIkTH4nsJqUYwfkkBUiB9r9p/ipQXbzn6QEK6Slw4zhkNxHsQPg/YjzE5UrUiR4CLv/rKLpTtPEBbowwfD2+Pr5YYzKp6vni9AvU7G7GezbpWlpT1IrSBfRwdaxed/7GP+hsNnP0iIyma3w5zRcGov1GkNV78hMyq6mBQJLrB05wneWbwLpeCdIfFEh1bR9c29fGDQZAiMgL2/OlZkE56iXYOaPHF1CwAenr2R5ONZJicS1d4fb8POBeAXAoO+BO8q+rPTjUmRUMmOZOQxbsZ6tIZxV8RySUwV72wTXBcGfAbKAr+9Abt+NjuRcMJNXRrSL74uuYU27vhyDdkFxWZHEtXVvt/hl+eN7f6fQFhjc/NUU1IkVKIim527vlpLWk4hl8ZGcPflzcyO5BqNLzXmUAD45nZIP2huHlFuSile6t+a2NpB7D6Rw+NzNslES8L1so4Zlyy1HbreB7G9zE5UbUmRUIleXrCdtQfSiQrx4+3B8Vgs1ehaWtf7HBMtnYJZtxiToAiPEODjxQfD2hHgY2Xu+sNMXXnA7EiiOrHbYPbIfyZM+usPDmEKKRIqyYJNR/j09714WRTv3eDBEyadK4sF+k8wJj1JWQWLnjY7kXBCs8gavNS/NQDPzt/K5kMZJicS1UbSS7DvNwiMhAGfGqvPCtNIkVAJDqTm8tCsjQA8elVz2jd085UdK0tAmDErmsXbWC1y61yzEwkn9IuPZminBhQW27lr6loy84vMjiSquLDUtbD0NaNP04BPoUYdsyNVe1IkVLCCYht3TV1LVkExvVvW4daLG5kdyVz1O0JPR+ejuWMgbY+5eYRTnr6mBS2igtmfmsvDs2QhKFGJMg7RfNubxna3x4y+TcJ0UiRUsJd+2M6mQxnUD/PnlQFtUDKm11iprfk1UJBprBhZXGB2IlFOft5WPhjWjiBfLxZsPsqkZfvMjiSqIlvDXcQxAAAgAElEQVQxzB6Jd3EWNL0Cut5vdiLh4JZFglKqt1Jqh1IqWSn1SCnP36eU2qqU2qiUWqyUaljiOZtSar3jNs+VuRdsOsKkZfvwtireG9qOEH9ZfAQwJj/p+x6ENoAj6+Hnp8xOJJzQKDyQVwe0AeDFH7axMSXd5ESiykl6EQ78SYFPmNGXyeKWv5qqJbf7n1BKWYH3gSuBFsBQpVSL03ZbB3TQWrcBZgGvlnguT2sd77j1dUlo/r8fwmNXNadtfQ9fuKmi+YfCgElG/4QVH8FWl9Zv4jxd1TqKm7o0pMimGTN1nfRPEBUneZExp4qysLXFAxAYbnYiUYLbFQlAJyBZa71Ha10ITAf6ldxBa71Ea53ruLscqOfijP+nZD+EXi1rM+KiRmbGcV/12kOPZ43tuWPg1D5T4wjnPHZVc1pEBXMgLZdHv5H5E0QFyDwC39xhbCc+RkZoFVvTpgpQ7vaNrpQaAPTWWt/muH8j0FlrPaaM/d8Djmqt/+u4XwysB4qBl7XW35Zx3ChgFEBERET7mTNnnnPmqdsK+Gl/MeH+imcv8ifQu+r0Q8jOziYoKKjiXlBrWm1+kfDUlWTWiGFdwktoi2delqnwtvEAR3PsPLMsj3wbjGjpQ2L90v/vqmPblJe0jUHZbbTd8BShGZtJq9mWjW2eJjsnT9qmDBX9uenWrdsarXWHs+3njgNQS/sNW2olo5QaDnQALivxcAOt9WGlVBPgF6XUJq317n+9oNYTgAkAcXFxOjEx8ZzCLtp6jJ9+XI2XRTHx1ouIr2KXGZKSkjjXtilT57bw8aUEZ+zisqIk6OWZazxUStt4AP/oQ9wzfT3TdhQzuHtnmkcF/2uf6to25SFt47DkJcjYDEG1CRs5i8SgSGmbMzCrbdzxckMKUL/E/XrAv5akU0p1Bx4H+mqt/+4ur7U+7Ph3D5AEJFRW0MPpeTwwawMAD/WOq3IFQqUJCHOs72CFP9+DnT+ZnUg4oV98NEM61qfAMX9CjqzvIJy19zdY+iqgjHUZgiLNTiTK4I5FwiogRinVWCnlAwwB/q+Xm1IqAfgYo0A4XuLxmkopX8d2OHAxsLUyQhbb7Iybvp703CIS4yK4rWuTynibqqt+J7j8CWP729HGtUnhMZ6+piWxtYPYcyKHp+dtMTuO8CQ5qcaaLtoOlz4ATS47+zHCNG5XJGiti4ExwEJgGzBTa71FKfWcUuqv0QqvAUHA16cNdWwOrFZKbQCWYPRJqJQiYfziXazcl0ZkDV9eH9i2eq3LUFEuHgdNukGu44eG3WZ2IlFO/j5W3ruhHX7eFmatSeHbdYfMjiQ8gdbw7X8g6wjUvxAu+9cId+Fm3K5IANBa/6C1jtVaN9Vav+B47Cmt9TzHdnetde3ThzpqrZdprVtrrds6/v20MvIt232Sd5ckoxS8PTie8CDfynibqu+v9R0CI4252n970+xEwgmxtWvw9DVGb/TH52xi38kckxMJt7f8Q9i1EPxC4fqJsi6DB3DLIsGdpeUUMm76erSGu7s146JmMqb3vARFQv+Pje2kF2H/MnPzCKcM6Vifq1tHkVNo4+5p6ygstpsdSbirw+v+mUit3/sQWv/M+wu3IEWCE7TWPPj1Bo5nFdCxUU3GXhFjdqSqoenl0PVe4xrl7NsgN83sRKKclFK82L819Wr6s+lQBq/+uN3sSMIdFWTBrFvBXgSdRkHzPmYnEuUkRYITJi3bx+Ltxwnx9+btIQl4WaX5Kky3x6FeR8g8BPPuNq5dCo8Q4u/N+KEJWC2Kib/v5Zftx8yOJNzN9w8Yi7vVbg09njc7jXCC/JYrpy2HM3jpB+OvpFeub010qL/JiaoYq7dxjdI3GLZ/B6s/MzuRcEK7BjV5oGccAA98vZH0fLnsIBw2TIeN08E7wBj67O1ndiLhBCkSyiG3sNi43mqzM6xzA3q3ijI7UtVUsxFc87axvfAxOFYpA1NEJbnj0iZ0bRZOWk4hn2wqwG6Xs0HVXupu+N6xouOVr0BErLl5hNOkSCiHZ+ZtYc+JHGJrB/Fkn9PXmhIVqtX1ED8civONa5hFeWYnEuVksSjeHNSWsEAftqTamfDbHrMjCTMVF8LskVCYDS2vg4QbzU4kzoEUCWcxf8NhZq5OwdfL4hgXbjU7UtV35StQqxmc2AYLHzc7jXBCZLAfrw80lpV+feEONhyUZaWrrV+eN0Y0hDSAPm8bS8YLjyNFwhkcTMvlsTmbAHiyTwtia9cwOVE14RtkXLu0+sDqT2HbfLMTCSdcfkFtejT0otiuGTt9HdkybXP1k7wYlo03pl6/fqKxVLzwSFIklKHYZmfcjPVk5RfTs0VthnVuYHak6iWqLXR3LCs9727IkBn9PMnAWB+aRwWzPzWXp77dbHYc4UrZJ2DOaGM78VFo0NncPOK8SJFQhvG/JLNm/ynqBPvxyvVtUHKqzPU6j4Zm3SHvFMy5Q6Zt9iA+VsW7Q+Px87bwzbpDMm1zdaE1zL0Tco5Dw65wyX1mJxLnSYqEUqzcm8Z7v+xCKXhrcDw1A33MjlQ9WSxw7Yf/TNv8+1tmJxJOaBb5z7TNT3y7mQOpuSYnEpVuxcew6ydj2uX+H4NF+nB5OikSTpORW8S46euwa7gzsSldmtYyO1L1FhRpFAoAS16Eg6vMzSOcMqRjfa5sVYfsgmLGTl9HkU3mT6iyjm6Cn580tvu+CyH1zM0jKoQUCSVorXnkm40czsgnvn4o47rLmF63ENMdLrwLtM0YUpWfaXYiUU5KKV7q35qoED/WH0znnUW7zI4kKkNhLswaCbZCaD8CWvQ96yHCM0iRUMKMVQdZsPkoQb5ejB+SgLdMu+w+uj8NddpA+v5/JmcRHiE0wIe3B8ejFLyflMzyPalmRxIVbeFjcHIHhMdBr5fMTiMqkPwWdEg+ns2z840Z/l64rhUNagWYnEj8Hy9fx5SuAbBpJmyYYXYi4YTOTWoxplsztIZ7Z6wnPbfQ7EiiomybD2s+N4YsD/gUfORnZ1Vy1iJBKTXEFUFOe8/eSqkdSqlkpdQjpTzvq5Sa4Xh+hVKqUYnnHnU8vkMp1as876eBe6avI6/IRv+EaPrFR1fY1yIqUHgM9H7Z2P7+fmPBGOExxl4RQ0KDUI5k5PPw7I1oWcTL82U4FmQD6PEc1Gltbh5R4cpzJuELpdQvSimXzEeslLIC7wNXAi2AoaW890jglNa6GfAW8Irj2BbAEKAl0Bv4wPF6Z3QqX7PlcCYNwgJ4tl/LivtiRMVrdxM07wuFWTD7drAVmZ1IlJO31cL4IQnU8PVi4ZZjTF910OxI4nzYbcbQ5LxT0KyHMWRZVDnlKRLaA97AOqXU60qpoErO1AlI1lrv0VoXAtOBfqft0w+Y7NieBVyhjIkM+gHTtdYFWuu9QLLj9c4os1BjtSjeGRJPDT/vCvtCRCVQCvqOh+B6cGg1JL1sdiLhhPphAfz3ulYAPDt/C8nHs01OJM7ZH+8YQ5MDI+DaD2TaZQ+SV1j+OWfOWiRorTdprS8BRgHDgR1KqaHnHu+sooGSf2KkOB4rdR+tdTGQAdQq57Gluq9HLAkNap5jZOFS/jWh/wRAwW9vwL7fzU4knNAvPpr+CdHkF9kZO20dBcUySZbHSVkDS14wtq/9yBiqLDzGCz+Uf4Vdr/LuqLWerJT6FngR+FIpNQoYo7Xe4nzEMyqtHD394mVZ+5TnWOMFjPyjAAJqN6I5B0lKSnEmZ7WQnZ1NUlKS2TFK1ajhQBrtn0n+tJtY3eEdir1du7aGO7eN2c7WNj1qaX7zV2w9ksnYiYsYeoGv68KZzNM/N9biXDqsvhd/ezEH6/Vl9yEvOJRUIa/t6W1TmSqqbdYeK2bKuoJy71/uIgFAa50B3KWUmgh8gXEJ4l3gGa11llNJy5YC1C9xvx5wuIx9UpRSXkAIkFbOYwHQWk8AJgA0i43Tl3frViHhq5qkpCQSExPNjlG6Sy6Gz/fil7KKrmkzYNCXLj3l6dZtY7LytE1UXDoDPlzGwn3F3HB5Oy6LjXBNOJN5/Odmzn8g/yjUbk39EROp71VxBZ7Ht00lqoi2OZqRz7h3ljp1TLmGQCqlvJVSnZRSY5VSU4HZGJ0DvYC7gO1KqYqaPWMVEKOUaqyU8sHoiDjvtH3mATc7tgcAv2ijq/Q8YIhj9ENjIAZYebY3tMqlNM9k9TZWmPMNNoZhrf3C7ETCCfH1Q7m3hzFh2f0zN3Ayu/x/3QiTbJoFG6aCl78x3LECCwRRuWx27Rh+XMSlThTk5RkCuQzIBP4E3gBigfnAYIy/1CMxOhfOUkqdd/dWRx+DMcBCYBswU2u9RSn1XIlC5FOgllIqGbgPeMRx7BZgJrAV+BG4S2stFzyrspqN4Oo3je0fH4ETO02NI5wz+rKmXNgkjJPZBTz49QYZFunOTu2D7+41tnu/BBFxpsYRzpmwdA9/7kklPMiHNwa2Lfdx5TmTkA28BPQEQrXWHbTW92itv9ZaH9ZaZ2qt7weeAB47p/Sn0Vr/oLWO1Vo31Vq/4HjsKa31PMd2vtZ6oNa6mda6k9Z6T4ljX3AcF6e1XlAReYSbazMQ2gyBolxj2uZi+YvUU1gtircGxxMa4M2SHSeYtGyf2ZFEaWzFxpDjgky4oI8x9bLwGBsOpvPGTzsAeG1AWyJqlP8MUHlGN/TUWj+ntV6stc45w65LMc4sCOF6V71mnFU4uhEWP2d2GuGEqBB/Xu7fBoCXftjO1sOyNofbWfoqpKyEGnWNxZtkuKPHyC4o5p7p6yi2a265uBHdLnBuJEpFTsu8gX/PZyCEa/gFw/WfgsUL/nwPkheZnUg4oXerOtzQuQGFNjtjp69zahy3qGT7l8HS1wBlLP8cEGZ2IuGEp+duYV9qLhfUqcHDvS9w+vgKKxK01nla6/kV9XpCOK1eB+jmuOI15z+QfcLcPMIpT17dgmaRQSQfz+b578s/jltUorxTxmUGbYdL7oPGl5qdSDhh7vpDzF6bgp+3hXeHJuDnfdYJiP9FFngSVcvF46DRJZBzHL79D9jtZicS5eTvY2X8kAR8rBamrjjAj5uPmh2petMa5t8DmSkQ3R4SHzU7kXDCwbRcnpizGYAn+7Qgpva5zSMjRYKoWixWuO5jY1bG5J9hxUdmJxJOaFE3mEeuNE6JPjx7I4fT80xOVI2t/QK2zgWfGsZQY6tMWe8pihyX7bIKiundsg43dGpwzq8lRYKoekKioe97xvaip+HIBnPzCKfccnEjusVFkJFXxL0z1mOzy7BIlzuxAxY8bGz3eRPCmpibRzhl/OJdrDuQTlSIHy9f3xp1Hh1NpUgQVVPzPtBhJNgKYdZIKDzTwBzhTpRSvDawLeFBvqzYm8YHS5LNjlS9FOUb3zPFecbQ4jaDzE4knLB8TyrvLUlGKRzDi33O6/WkSBBVV68XIKI5pO4yJloSHiM8yJe3BhsTvry9eBdr9qeZnKgaWfQMHNsENRvD1a+bnUY44VROIeOmr0drGNOtGRc2qXXerylFgqi6vB1Tx1p9jeurm78xO5FwwiUxEdxxaRNsds3YaevJyCsyO1LVt/MnWPGhMZR4wKfg69pF08S501rz0OyNHM3Mp12DUO65IqZCXleKBFG11W5pnFEAmD8OTu03N49wyv0942hTL4RD6Xk8NmeTTNtcmTKPwLeOmfUvf8IY0SA8xpTl+/l56zFq+HnxzpAEvKwV8+tdigRR9XW8zZhKtiDDmLbZJn+RegofLwvjhyQQ6GPl+41HmLHqoNmRqia7DeaMgtxUaNINLrrH7ETCCduOZPL899sAeLl/G+qHBVTYa0uRIKo+pYypZIOjIWUVLHnR7ETCCY3CA3n+2lYAPDN/C7uOVdSq9OJvv78Fe5dCQLgxhNgivxo8RV6hjbunraOw2M7QTvW5uk1Uhb6+fBJE9RAQBv0/AWUxfiDuSTI7kXBC/3b16N8umvwiO3dPW0d+kUzbXGEOrvyncL7uY6hR29w8winPfbeF5OPZNIsM4qk+LSv89aVIENVHo4vh0ocADd/cATknzU4knPB8v1Y0Dg9k+9Es/ivTNleMvHRjuKO2QZcxENPd7ETCCd9vPMK0lQfx8TKmXfb3cX7a5bORIkFUL5c+CA0uguyjMGe0TNvsQQJ9vXh3qDFt85TlB/hx8xGzI3m2v6ZdzjgAdRPgiqfNTiSccCA1l0dmbwTgiaub0zwquFLeR4oEUb1YveD6T/6Ztnn5+2YnEk5oFR3y97TND83aSMqpXJMTebA1n8PWb8EnyFhB1ev8Jt0RrlNYbOdux7TLvVrW5sYLG1bae7lVkaCUClNK/ayU2uX4t2Yp+8Qrpf5USm1RSm1USg0u8dwkpdRepdR6xy3etV+B8Agh9aDfB8b2omcgZY2pcYRzbrm4EVdcEElmfjH3TF9PkU3OBjnt6Gb40bFg0zXvQK2m5uYRTnn9px1sOJhOdKg/r17f9rymXT4btyoSgEeAxVrrGGCx4/7pcoGbtNYtgd7A20qp0BLPP6i1jnfc1ld+ZOGRLrgKOv8H7MUwa4RxbVZ4hL+mba4T7Mea/ad48+edZkfyLIU5MOsWKM6HhBuh9QCzEwknLNlxnAlL92C1KMYPjSckoHIX3nK3IqEfMNmxPRm49vQdtNY7tda7HNuHgeNAhMsSiqqjx7MQFQ/pB2D+WOMarfAIYYE+jB+agEXBh0m7+XXnCbMjeY4fHoSTOyHiArjyVbPTCCcczcjn/pnGgnX394ylfcOwSn9PdysSamutjwA4/o08085KqU6AD7C7xMMvOC5DvKWU8q28qMLjefnCwM+NpXC3zoXVn5mdSDihU+Mw7usRC8B9M9ZzLDPf5EQeYMN0WP8VePnDwEngU3GT7ojKZdeacTPWkZZTyCUx4Yy+1DWXiJSrpzlVSi0C6pTy1OPAZK11aIl9T2mt/9UvwfFcFJAE3Ky1Xl7isaMYhcMEYLfW+rkyjh8FjAKIiIhoP3PmzHP+mqqy7OxsgoKCzI5RqSKPLaXFtjewK2/WtH+NnKDG5TquOrTNuXJV29i15o3V+WxJtXNBmIWHOvphqcTrsxXBrM+Nf24KHVbfj9Wez/a4MRyN6uHyDGcj31Nlm7ElmwUHFcE+iucu9iPU9/z+xu/WrdsarXWHs+3n8iLhTJRSO4BErfWRv4oArXVcKfsFYxQIL2mtvy7jtRKBB7TWfc72vnFxcXrHjh3nlb2qSkpKIjEx0ewYlW/eWFg7GWo1g1FJ5VrYptq0zTlwZdscz8rnqnd+52R2AeO6xzCue6xL3vdcmfK5KcqDid3h2GZoNQCun2jMROpm5HuqdL/vOsmNn64ABV+N7MxFzcLP+zWVUuUqEtztcsM84GbH9s3A3NN3UEr5AHOAL04vEByFBcro6nktsLlS04qq48pXILIlpCYbC0G5UfEsziyyhh9vD45HKXhn8S6WJcskWf+y4GGjQAhrCn3ecssCQZTueGY+42asQwP3XBFTIQWCM9ytSHgZ6KGU2gX0cNxHKdVBKTXRsc8g4FJgRClDHb9SSm0CNgHhwH9dG194LG9/GDQZvANh8yxjDLnwGF1jwhnTrRlaw9jp6zmeJf0T/rZxpnGWzMvP+Iz7Vc6kO6Li2eyasdPXcTK7kOZhFu6+vGKWf3aGWxUJWutUrfUVWusYx79pjsdXa61vc2xP0Vp7lxjm+PdQR6315Vrr1lrrVlrr4VrrbDO/HuFhwmOMMeMACx6BIxvMzSOcMq57LBc2CeNkdgFjp63DZpezQZzYaZwZA+NsWZ3W5uYRTnln8S6W70kjPMiXO9r6YrW4/gyQWxUJQpiuzUBoPwJsBfD1CMjPNDuRKCerRTF+SALhQb4s35PG24uq+fwJhbnw9c1QlAOtB0K7m89+jHAbv+86ybu/7EIpGD8k/rw7Kp4rKRKEOF3vl6F2a0jbA/Pulv4JHiQy2I/xQ+OxKHhvSXL1nj9hwYNwfCvUioE+b0s/BA9yJCOPsdPXGZfPLnd9P4SSpEgQ4nTef40hDzLmtl85wexEwgkXNQ3n3u6xaA33zljPkYw8syO53ropxs3L0dfGV4YVeooim50xU/+ZD2HsFa7vh1CSFAlClCa8GfR7z9he+DgcXGVuHuGUu7o149LYCNJyCrl76rrqtb7DkY3w/f3G9tVvQO2W5uYRTnllwXbW7D9FnWBj1I4Z/RBKkiJBiLK0vM6xvkORcW03R4bWeQqLRfHWIGN9h9X7T/HSD9vNjuQaeekw8yZjXYZ2N0HCMLMTCSf8uPkIE3/fi5dF8f6wBGoFmT9psBQJQpxJj+egXifIPASzbwO7zexEopxqBfnywfB2eFsVn/2xl+82HjY7UuXSGubeBaf2Qp02cOVrZicSTth3MocHv94IwKNXNXfJugzlIUWCEGfi5WP0TwioBXuWwK+vmJ1IOKFdg5o8cXULAB6etZHk41kmJ6pEy8bD9u/ALwQGfQHefmYnEuWUV2jjP1+tJaugmCtb1eHWixuZHelvUiQIcTYh0XD9p4CCX1+FXYvMTiSccFOXhvRtW5ecQhujp6wlp6DY7EgVb98fsOhZY/u6jyGsfOuPCPNprXlszia2HcmkcXggrwxog3KjkShSJAhRHk27QbfHAQ2zR0LaXrMTiXJSSvFS/9bERAaRfDybh2dvxJ3WrDlvmYeNPjPaBl3vhbgrzU4knPDFn/uZs+4Q/t5WPhrenmA/b7Mj/R8pEoQor0vuh9grIT8dZtyIxVZgdiJRToG+Xnw4vD2BPla+23iET3+vIkVecYHRUTHnBDS+DLo9YXYi4YTV+9J4/rutALw6oA1xdc6+sJyrSZEgRHlZLND/Y2ORnGObiNvxvky05EGaRQbx+sC2ALy0YDvLdleB0SoLHoaUVRBSHwZ8DlYvsxOJcjqelc+dX62l2K4Z2bUx17Sta3akUkmRIIQz/EJg8BTwDqT28V9hxcdmJxJOuLJ1FHcmNsVm14yZuo5D6R480dLaL4yFyKy+MPhLCKxldiJRTkU2O2O+WsfxrAI6NQ7jkSsvMDtSmaRIEMJZtVv8M9HST48bncaEx7i/Z9zfEy2N/nIN+UUeOKz10Br4/gFju89bUDfB3DzCKf/9bisr96VRO9iX925IwNvqvr+K3TeZEO6sVX8O1L8O7MVGp7GMFLMTiXIyFoKKp36YP5sOZfDYnE2e1ZEx+zjMuMlYhKzDSJkwycPMXHWQyX/ux8dq4YNh7Yms4d5DVaVIEOIc7W18o9FZLOcETB8GRR586rqaCQ3wYcKNHfD3tvLN2kNMXrbP7EjlU1wIM26EzBRjkq/eL5udSDhh7YFTPPHtZgCev7Yl7RvWNDnR2UmRIMQ50harMdFSzUZwZD3MHSMdGT1I86hgXhnQBoDnv9/GsmQ378ioNfzwABxcDjXqGn1jvHzMTiXK6VhmPqO/XEOhzc5NXRoyuGMDsyOVi1sVCUqpMKXUz0qpXY5/Sy2zlFI2pdR6x21eiccbK6VWOI6foZSS7yBRuQLCYMg08A6EzbPgj7fNTiSc0LdtXUZfZnRkvHPqWvan5pgdqWyrJsLayeDlB0O+ghq1zU4kyim/yMYdX67heFYBnRuH8WSfFmZHKje3KhKAR4DFWusYYLHjfmnytNbxjlvfEo+/ArzlOP4UMLJy4wqB0ZGxv2OUw6JnYedP5uYRTnmwVxyXXxBJem4Rt01eTVZ+kdmR/m3vb8ZwR4C+70J0O3PziHLTWvPEt5tZfzCd6FB/PhjWzq07Kp7O3ZL2AyY7ticD15b3QGXMY3k5MOtcjhfivDS/BhIf4+8ZGU/sNDuRKCerRfHOkHiaRQax63g246avx2Z3o8tGp/YZEyZpG1w0FtoMMjuRcMKEpXuYtSYFP28LH9/Y3i1WdnSGuxUJtbXWRwAc/0aWsZ+fUmq1Umq5UuqvQqAWkK61/mti9hQgunLjClHCpQ9C875QkAlTB0FumtmJRDnV8PNm4k0dCPH3ZvH247zx0w6zIxnyM2DqEMhLg2Y9oPszZicSTvh56zFe/tFYpvzNQfG0ig4xOZHzlKuH/iilFgF1SnnqcWCy1jq0xL6ntNb/6peglKqrtT6slGoC/AJcAWQCf2qtmzn2qQ/8oLVuXUaOUcAogIiIiPYzZ848z6+sasrOziYoKMjsGG6ptLax2PJJWPcoNbL3kB7Skg1tn0Vb3Gsudlfw1M/N1lQbr6/Ox67h9tY+XBxd8f935W0bZbfRavN/qZW2lpyAeqxLeIVib89rU2d46uemNAez7Px3eR4FNugf403fpufXRa6i26Zbt25rtNYdzrafy4uEM1FK7QAStdZHlFJRQJLWOu4sx0wCvgNmAyeAOlrrYqVUF+AZrXWvs71vXFyc3rHDTf5ycDNJSUkkJiaaHcMtldk2GYdg4hWQdQTih0G/98GNVnVzBU/+3Hzx5z6emrsFb6tiysjOdG5SsTMZlrttfngQVk4wlim/bXG1WNnRkz83JZ3IKuDa9//gUHoe18bX5a3B8ee9smNFt41SqlxFgrtdbpgH3OzYvhmYe/oOSqmaSilfx3Y4cDGwVRvVzhJgwJmOF6LShUTD0Gng5Q/rv5IRDx7mpi6NGHFRI4psmjumrGHvSRNGPKyYYBQIVh8Y/FW1KBCqivwiG6O+XM2h9DwSGoTy8vXutfSzs9ytSHgZ6KGU2gX0cNxHKdVBKTXRsU9zYLVSagNGUfCy1nqr47mHgfuUUskYfRQ+dWl6If5SNwH6TzC2Fz0DW+edcXfhXp7s04IrHCMebp20ilM5ha57812L4Me/RjK8Bw27uO69xXmx2zX3f72BdQeMkQwTbuyAn7fV7Fjnxa2KBK11qtb6Cq11jOPfNMfjq7XWtzm2l2mtW2ut2zr+/bTE8Xu01p201s201gO11rKWrzBPi77/dDT7ZhSkrDYzjXCC1aIYPzSBFqz/DdUAACAASURBVFHB7D2Zwx1T1lBQ7II1Ho5uhq9HgLYbHWHbDq789xQV5pUft/P9xiPU8PXi0xEdiKjhWSMZSuNWRYIQVc7F4yBhOBTnwdTBkLrb7ESinAIdP+hrB/uycm8aj8yu5DUeMlLgqwFQmAUt+zuG1ApP8eWf+/h46R68LIoPh7fngjrBZkeqEFIkCPG/9u48Lupqf/z468wAsgqyuCSoGEoaLriRmghpLmmppGWpaW51W+x2s8Vv2q37zeze31evbdc0dzJTK9NKr2mKW66kiaYmoRaC4oogO5zfH5/RXEAoYT4DvJ+Pxzycz2c+M/Oe4zDznnPO530qklLQdzrc3g2yzhhfApccvPyvuKKetxtzhrfH3cXK8j0n+NeaCprgnH0BPh5oTHZt2Bn6zwCLfDxXFut+OsXfVx4AYEpMC+5u4m9yROVH3oVCVDSrMzy0AOq2hHNJRo9CXpbZUYkyCqvvzX+GtMFqUcyI+6X8F4MqyIUlQ+H0QfAPNUouOzv2yoDid/uSL/Ds4j0UaXiuWxMGtQsyO6RyJUmCEPZQwwuGLAPvBnBit1GVscgOY9yiXESF1ubtGKPkyutfHWB1Qmr5PHBREXz5Fzi2GTzrwtDPwM3xVwYUhqNnLjFy/i6y8wt5sE0gf+3exOyQyp0kCULYi5ftS8DVBw6vgm9ekFUjK5FB7YJ4sWcoWsNzS/ay8+gtVtTUGtZOgv2fg4unkUT6VI6VAYWxquOwOTs4k5lHlyb+TIlpUalPdSyJJAlC2FNAKDzyqbGSX/w8WP+/Zkck/oCnom5n6F0NyCsoYvSCXRw+mfHnH2zLNNj2Plic4aGFUK9l+QUqKlR6dj7D5+4k+Xw2rYJ8+HBoW1ycqubXadV8VUI4soYdYdB8UFbYPBW+f9/siEQZKaV444EwejSvw8WcAobO2fHnlpfePRe++wegjBVEQ7qVe6yiYuTkFzJ6wS4OncygcYAH80a0x6OGk9lhVRhJEoQwQ2hv6P8f4/q3r8Kej82NR5TZ5RoKHRv7cTojlyGzd3AyPafM9w9I2wxf/83Y6DMVwh6soEhFeSsoLOKZT35g17Hz1K3pSuyoCHw9bm1NBkcnSYIQZmk1GHr907i+8lk4+LW58Ygyc3W28tHwdrQK8iH5fDZD5+zgXFmqMiauo9nB6YCGeyZB+1EVHqsoH4W2aorrDqbh7eZM7KgO1PdxMzusCidJghBmuutJ6PqyUWHvs8chcZ3ZEYky8qzhxILH2xNax4vEtEyGz91JRk5+yXc4thWWDMOiC6DjM9DlBfsFK25JUZFmwhf7WLE3BQ8XK/Meb0+TOl5mh2UXVXcg5Rbl5+eTnJxMTk7ZuxGrIm9vb44ePUpgYCDOztVvyWO7iJpgFNPZORM+HQKPLoXGXc2OSpSBj7sLsaM6MPDDbSScSGfU/N3Me7yYMepft8OiQZCfRWrd7tTr8Wa1Wxm0stJa8/eVB1i6OxlXZwtzR7SnTYPqc5qqJAklSE5OxsvLi0aNGlXJ01rK6uLFi+Tl5ZGcnExwsKxEVyGUgt7/hMI844yHxYNhyGfQqLPZkYkyqF3TlUWjIxj04TZ2HjvHyPm7mPd4e9xdbB+vybuNaor5l6Dlwxyu9TD1qvFnSmWiteatVQeJ3X4cFycLHz3WrtyXDnd0MtxQgpycHPz8/Kp1ggDGbG4/P79q36NS4ZSCPtOg9VDIzzJ+df663eyoRBkF+bqzeOxd1KlZgx1HjUQhK68AUvZAbMzv6zH0+49xVotweFprpn77Mx9tPmqsxzCkDV2aBJgdlt1JknAT1T1BuEzawU4sFnjgXWg52PjV+fFA+G2X2VGJMgr292DxmLuo7VWD7Unn+MdHS9AL+0NuOjR7wFg63Cqdt5WB1pp/rTnM+xsSsVoU7z0STrdmdcwOyxSSJAjhSCxW49TIsAeNX5+x/Y0Jb6JSaBzgyeKxdxHl8SsT0sajci5Q2KQ3PDjHWMNDODytNZO/OciMuF9wsijeHRxO7xb1zA7LNJIkCOFoLFYYMAtaDIK8TPj4QfhlvdlRiTK6PWsfcy1v4q2yWFPYjmEX/0JGgfTGVQZFRZrXVx5g9pajOFsVHwxpQ5+W1TdBAAdLEpRSvkqptUqpI7Z/b5hCqpSKVkrtveqSo5Tqb7ttvlLq6FW3tbb/q6h4nTp1KvWY7OxsunbtSmFhyYsI5eXlERkZSUFBQXmGJ8qD1QkGzITwYVCQbawceXi12VGJ0vyyHmJjsORnktGkH//r9hLfH8/k0Y/KWEdBmKaoSPPql/tZsO04LlYLM4e1peeddc0Oy3QOlSQArwDfaa2bAN/Ztq+htd6gtW6ttW4N3ANkAd9edciLl2/XWu+1S9R29v3335d6zNy5c4mJicFqLXmSlIuLC926dWPJkiXlGZ4oLxYr3P8udBhrnPmwZCjs/8LsqERJDq82krmCbGg9FK9H5rH4yS409HMn4UQ6D8/cxqmLMgHYEeUVFPH80r0s3vkrNZwszB7ejnvuqJ5zEK7naElCP2CB7foCoH8pxw8EVmutsyo0KpNcunSJPn360KpVK8LCwq58mXt6enLs2DGaNWvGmDFjuPPOO+nRowfZ2dlX7rto0SL69et3ZTs6Opq1a9cCMHHiRMaNGwdA//79WbRokR1flfhDLBbo/S/o/BwUFRhLTO+aY3ZU4np7FxtJXGEetB8DD7wHFitBvu4se6IjTet4ciQtk0EfbuN0VpHZ0YqrXMotYNSCXdcUSopsWv3OYiiJ0g60VK1S6oLW2ueq7fNa6xKrViil1gPTtNZf27bnAx2BXGw9EVrr3BLuOxYYCxAQENB26dKl19zu7e1NSEgIAC0mb7qFV1WyhFcjb3r7ihUrWLduHe+99x4A6enpeHt7U69ePbZv307r1q3ZuHEjLVu2ZPjw4fTu3ZvBgweTl5dH8+bNSUxMvPJYW7duZfLkyQwfPpxly5axZMkSrFYrhYWFhISEcPTo0WJjKCwsxGq1kpiYSHp6evm9+CogMzMTT09P+zyZ1jQ8vozgY0ZCd6zhQxxr9KjDFuSxa9uYSWsa/Po5jY/GAvBrUAxJjR+74f8lM08zdXcORy8WUdNF80I7NxrWlFMhr2fv901Gnubf8TkkpRfh5QIvtHWlkbdj/r+Ud9tER0fHa63blXac3c/HUUqtA4ob6Hn1Dz5OPaAFsOaq3ROAk4ALMAt4GfhHcffXWs+yHUNoaKiOioq65vaDBw/i5VWxZTdLe/wOHTowadIk3nzzTfr27UuXLl2u3Obp6UlwcDCdOxsFdyIiIjh16hReXl6kpKRQq1atax6/V69eTJkyhRkzZhAXF3fNbTVq1CgxnoyMDLy8vHB1dSU8PPyWXm9VExcXx/Xvm4oVDfER8PVfaXR8KY18XaDvdIecNW//tjFBUSH89xU4Ggso6PU2De56kgYlHN41Mp+xC+PZlnSWf+3O54MhLYgKrW3PiB2ePd83yeezeGzuTpLSiwis5UbsqAiC/T3s8tx/hll/U3ZPErTW3Uu6TSl1SilVT2udaksC0m7yUA8By7XWV4qla61TbVdzlVLzgPHlEfOxt/uUx8P8YU2bNiU+Pp5Vq1YxYcIEevTowWuvvXbl9stf7gBWq/XKcIObm9sNxY8SEhJITU3F39//hmQgNzcXV1fXCnwloty0HQ6edWDZCGPlyMw0Y9lpF8f9cKuS8nPgizFwcCVYXYxJpmExN72Ll6sz80e2Z/gHa9meWsioBbt5a0AYD7cvKa0QFWVf8gVGL9hNWkYud9T1YuHIDtSuKZ+BxXG0OQkrgeG268OBFTc59hFg8dU7bIkFyqj+0x/YXwEx2k1KSgru7u4MHTqU8ePH88MPP5TpfrVq1aKwsPBKopCamsqQIUNYsWIFHh4erFnze+fL2bNnCQgIkHUZKpPQXjD8K3DzhSPfwrzekJ5sdlTVR8YpWHC/kSDU8IahX5SaIFxWw8nK2JY1eCrqdgqLNC9/nsC0bw/jSMO+Vd2qhFQemrmNtIxcIoJ9WfJER0kQbsLRkoS3gXuVUkeAe23bKKXaKaVmXz5IKdUICAI2Xnf/RUqpBCAB8AfetEPMFSYhIYEOHTrQunVrJk+ezMSJE8t83x49erBlyxaysrKIiYlh6tSpNGvWjEmTJvH6669fOW7Dhg3cd999FRC9qFBB7WHUWqgVDKk/wqxoqc5oD6k/wkfRkLwTagbCyNUQ3KX0+13FohQv9bqDyQPCsCh4d30izyzeY5RxFhVGa83764/w1KIfyMkv4qF2gcSOisDbTX4g3YxD1QjVWp8FuhWzfzcw+qrtY0D9Yo67pyLjs7eePXvSs2fPG/ZnZmYCsH//7x0l48dfO7LyzDPPMG3aNLp37862bduu7I+MjLxm+5NPPmHKlCnlHbqwB/8QGLMelg2Ho5tg/n3GKZOtHzE7sqrpwHJY/hfjFMegCHj4Y/D883MKhkQ0pJ63K+MW7+Wbfan8kpbJR4+1I8jXvRyDFgA5+YVM+CKB5XtOoBRM6H0HY7o0lpLzZeBoPQminISHhxMdHV1qMaX+/fsTGhpqx8hEuXL3Nbq7248xTr/78kn4diIUyq/SclNUBBveMuaB2GogMPyrW0oQLrvnjjp8+XQngv09OHQygwfe38L3iWduPWZxxW/nshj44fcs33MCdxcrs4a1Y2zk7ZIglJEkCVXYyJEjSy2m9Nhjj9kxIlEhrM7Q5/+g77/B4gTfvwcLH4CLqaXfV9xc5mn4OAY2/hOUBXq+Bf3eB6capd+3jEJqe/Hl052JCg3gfFY+w+buZPbmJJmnUA7WHzpF3/e2sP/ERYJ83Vj2ZEfubS5Fkv4ISRKEqCrajYTHVoJnXTi+FT68W9Z8uBXHtsLMLpC0Adz9YMhn0PHpCqlN4e3mzJzh7a9MaHzzm4OMXrBbSjn/SYVFmv9bc5iR83eTnp1P92a1+fqZLtx5m7fZoVU6kiQIUZU06gxPbobGUZB1BmJjYP1k45x+UTZFRbB5KizoCxmp0KATPLkFQm6YLlWurBZjQuOHQ9tS09WJ7w6l0fudTWxPOluhz1vVpFzIZujsHby/IRGLgpd6hTJrWDu83WWC4p8hSYIQVY1nbWOeQtT/GNub/gXz7oOzv5gbV2Vw4VeI7Qff/QN0Edz9vDH/oOZtdguhV1hdVj3XhbYNa3HqYi6PfrSdaWt/pqBQyjmXZsXeE/ScvoltSWfx93Th41ERPBUVgsUi8w/+LEkShKiKLFaIehkeW2EMP/y23Rh+2DHL+KUsrqU1xC+A/3QyzhRx94NHl0H3140VOe0ssJY7S8bexTPRIWjg3e+OEDPjew6dvGj3WCqDC1l5PLt4D899upeMnAK6N6vN6uci6RTib3ZolZ4kCUJUZY27wlPboMUgyM+C1S8av5TPHzc7MsdxMQUWDYKvxkFeBjS7H57aAU17mBqWk9XC+J6hLBoVwW3eruxLTuf+97bwzroj5BVIonfZ2p9O0Wv6Zr76MQV3Fytvx7Tgo8faEeBVfpNLqzNJEoSo6tx94cHZ8FAsuPsbv5RndIKt70Jhfun3r6oKC2DHTPjgLkhcC64+EGNrJ0/HWQWwU4g/a56PZOhdDcgv1Px73c888P4W9iVfMDs0U524kM2YhbsZs3A3Jy/m0LZhLVY/14XBHRrI6Y3lSJIEIaqL5g/AU9uheT/Iy4S1k2BGZ0i6vnBpNXB8G8zqCqtfgtx0aNrLaJuWgxxyZU0vV2fe7N+CT8ZE0MDXnUMnM+j3wVZe+XwfZzKLXei2ysovLGLWpl/oPnUja386hWcNJ16/vzlLn+hIQz9Zw6S8SZJQxWVnZ9O1a9dSiypFRkZSUCAFeKo8zwB4aCEM+Rx8G8OZw0ZNhWUj4MJvZkdX8S6mwhdPwLxecGo/eDeAwZ/AI59CzXpmR1eqTrf789+/dmFMl2CsSvHprt+I/n9xfLQpqcoPQWitWfvTKe57ZzNvrTpEdn4hfVrU47sXujKiczBWmZxYISRJqOLmzp1LTExMqUWVunXrxpIlS+wYmTBVk+7GL+d7JoKTm1Fy+L02sOolYwGjqubSGVjzKrzbGvZ9CtYaEPkSPL0D7ujjkL0HJXF3ceLVPs1Z83wkUaEBZOQWMHnVQXpO38RXP6ZQWFT1ijDtPnaOQR9uY8zC3RxJy6SBrzvzHm/PB0PaUEcWZ6pQkiQ4uIULF9KyZUtatWrFsGHDAJg2bRphYWGEhYUxffp0AC5dukSfPn1o1aoVYWFhV77wFy1aRL9+/a48XnR0NGvXrgVg4sSJjBs3DoD+/fuzaNEie740YTanGhD5IjyzC8IeNMo675wJ77SCta9B1jmzI7x12Rdg/ZvGa9r2PhTkGBMTn94O97wKLpV3nYTbAzyZ/3gH5o1oT2N/D46eucSzi/fQc/omVuw9USWShctLOg/8cBu7j5/H18OFv9/fnLV/iyQ69NbLYovSOdQCTw7r9Qqq0vV6+k1vPnDgAJMnT2br1q34+/tz7tw54uPjmTdvHjt27EBrTUREBF27diUpKYnbbruNb775BoD09HTy8vJISkqiUaNGVx7zjTfe4LXXXiMtLY09e/awcuVKAMLCwti1S1YRrJZ8gmDgXLj7bxA3BQ59DVvfgV1zoPUQiHgC/G43O8o/5vwx43TPPbGQazttsEkPiP4fuC3c1NDKW/Qdtekc4s9n8cl8sCGRxLRMnvt0L+9+d4S/RIXQt2U9XJ1L7kl0NEVFmrif05i5MYkdR41E1d3Fyui7gxkT2RgvVymKZE+SJDiw9evXM3DgQPz9jXN9fX19iY2NZcCAAXh4GBN0YmJi2Lx5M7169WL8+PG8/PLL9O3bly5dupCSkoKPj881jxkZGYnWmmnTphEXF3dlGMJqteLi4kJGRgZeXl72faHCMdQNg8GL4ES8saBR4jqjZ2HnLOML9q4noXG043bNaw3HtsCOD+HwKqMYEkCjLsawSoO7zI2vArk4WXg0ogED2wbyxQ/JvL8hkV9OX2L8sh+Z/M1PPNQuiCERDWng57g9J+nZ+Wz4NZ83p28iMc1Y6darhhOPRDRgdJdganvJsIIZJEkoi1J+8VcUrfUNp/KUtOhL06ZNiY+PZ9WqVUyYMIEePXrw7LPPkpOTc81xCQkJpKam4u/vf0MykJubi6ur/CFWe/XbwtDP4eR+4wt331I4ssa4+DSEFgMhbCDUaW52pIbTP8P+zyDhMzhnqyppdTGGUCKeqHI9Bzfj4mRhcIcGPNg2kC/3nGDBtmPsP3GRmZuSmLU5icgmAQwIr889zWpT0wF+kRcUFrE58Qyfxyfz7U+nbJMv86jn7crIzsE83CHIIeKszhwqSVBKDQJeB5oBHbTWu0s4rhfwDmAFZmut37btDwY+BXyBH4BhWutKu0JKt27dGDBgAM8//zx+fn6cO3eOyMhIRowYwSuvvILWmuXLlxMbG0tKSgq+vr4MHToUT09P5s+fT61atSgsLCQnJwdXV1dSU1MZMmQIK1asYNy4caxZs4aePXsCcPbsWQICAnB2lj9IYVM3zFjxsPsbED8Pds+FC8eNdQ02T4Xad0LYALi9G9RrZVR5tIeiIjiVYCxetf8LOLnv99s860Dbx43Frryq72p/zlYLg9oFMbBtID8mpxO77Thf7Uth48+n2fjzaVysFu5u4k/vsLp0a1YHXw8Xu8V2MSefrUfOsPHn03x3KI3TGcYpnEpBM18LT9zbkj4t6+FslSlzjsChkgRgPxADzCzpAKWUFfgAuBdIBnYppVZqrX8C/gn8W2v9qVLqQ2AUMKPiw64Yd955J6+++ipdu3bFarUSHh7O/PnzGTFiBB06dABg9OjRhIeHs2bNGl588UUsFgvOzs7MmGG87B49erBlyxY6depETEwMU6dOpVmzZkyaNImXX375SpKwYcMG7rvvPtNeq3BgHn4QOd5Yx+D495CwDH5aAWkHYP0BY2Kgq7fRrd84CgLbYSksx3P383OMUzVPxBs1HY5uguyrJlXW8DYmI7YYaMRgQhllR6WUonWQD62DfJjYpxkrf0xh9f5Udh49x/pDaaw/lAZA0zqedAj2pUOwH+0b1aJuTddyK0iUdjGH/SnpJCRfZOsvZ/jh+HkKrppUGezvwYNt6jOgTSBH9u4gKrx+uTyvKB/KEdcsV0rFAeOL60lQSnUEXtda97RtT7Dd9DZwGqirtS64/ribCQ0N1YcPH75m38GDB2nWrNmtvRAHsGfPHqZNm0ZsbOxNj4uJiWHKlCmEhoZes//yHIWq0h7lKS4ujqioKLPDMEdBHvzyHRxeDUc3GhMFr6KxoPwaQ+3mEBBq/ML3CDAunrWNMyuuf7xLp+FSGmTaLmd+hrSfjIWp9HV1PryDjJLTTXtByL3gXHmGyRzhfXM6I5dvfzrJ6oST7Dp2jtzraizUdHWicYAnjQM8uD3Ak/o+bni7OVPTzRlvN2e8XJ0o0pr8Ak1eYREFRUVcyMrnZHoOqek5nEzP5rfz2ew/kU5axrUJo9WiaNugFl1DA+jaNIA7b6t5JSFxhLZxVOXdNkqpeK11u9KOq4wpd33g6qovyUAE4Adc0FoXXLW/2qek4eHhREdHU1hYWGKthLy8PPr3739DgiBEiZxcILS3cQEjSUjaCMc2w8kEY57A2UTjcvAWn0tZwK8J1G0BwV0guKtRCMpRJ1BWAgFeNRgS0ZAhEQ3JLSgkITmdHUfPsfPoOX749TwXcwrY+9sF9v5266WfvWo4cWf9mrSo7014g1p0DvHH202GNSsLu/ckKKXWAXWLuelVrfUK2zFxlNyTMAjoqbUebdseBnQA/gFs01qH2PYHAau01i1KiGMsMBYgICCg7dKlS6+53dvbm5CQkD/1GquSy8lFYmIi6enmTOB0VJmZmXh6epodhkPKunie2uo8HpeO45adikveBZzz06/8aym6trqnVlbyXLzJd/Yhz8WHPBdvst3qccmjIVnugRRZq85iPY7+vtFak56nOXlJk5pZxMlLRZzP1WQVQFa+5lK+JrtAY1EKJwtYFThZwM1J4etqXGq5WvB1VQR5WajtrrCUMaFz9LYxU3m3TXR0tGP2JGitu9/iQyQDQVdtBwIpwBnARynlZOtNuLy/pDhmAbPAGG64vhvn4MGDciogvw83uLq6Eh5efWaJl4V0jZYsLi6OdlED/tB93CooFkcj75uSSduUzKy2qYzTR3cBTZRSwUopF2AwsFIbXSIbgIG244YDK0yKUQghhKj0HCpJUEoNUEolAx2Bb5RSa2z7b1NKrQKw9RI8A6zBGO1cqrU+YHuIl4G/KaUSMeYozLmVeBxxUqcZpB2EEKJ6cqiJi1rr5cDyYvanAPddtb0KWFXMcUkY8xNumaurK2fPnsXPz69ar02utebs2bNSZEkIIaohh0oSHElgYCDJycmcPn3a7FBMlZOTg4+PD4GBgWaHIoQQws4kSSiBs7MzwcHBZodhuri4OJmwKIQQ1ZRDzUkQQgghhOOQJEEIIYQQxZIkQQghhBDFcsi1G+xNKZUBHC71wOrJH6NQlbiRtE3JpG1KJm1TMmmbkpV32zTUWgeUdpBMXDQcLkt5yupIKbVb2qZ40jYlk7YpmbRNyaRtSmZW28hwgxBCCCGKJUmCEEIIIYolSYJhltkBODBpm5JJ25RM2qZk0jYlk7YpmSltIxMXhRBCCFEs6UkQQgghRLGqdZKglOqllDqslEpUSr1idjyORCk1VymVppTab3YsjkQpFaSU2qCUOqiUOqCUes7smByJUspVKbVTKfWjrX3eMDsmR6KUsiql9iilvjY7FkejlDqmlEpQSu1VSu02Ox5HopTyUUp9ppQ6ZPvs6Wi3566uww1KKSvwM3AvkAzsAh7RWv9kamAOQikVCWQCC7XWYWbH4yiUUvWAelrrH5RSXkA80F/eNwZlLJnqobXOVEo5A1uA57TW200OzSEopf4GtANqaq37mh2PI1FKHQPaaa2lTsJ1lFILgM1a69lKKRfAXWt9wR7PXZ17EjoAiVrrJK11HvAp0M/kmByG1noTcM7sOByN1jpVa/2D7XoGcBCob25UjkMbMm2bzrZL9fwlch2lVCDQB5htdiyi8lBK1QQigTkAWus8eyUIUL2ThPrAb1dtJyMf9uIPUEo1AsKBHeZG4lhsXep7gTRgrdZa2scwHXgJKDI7EAelgW+VUvFKqbFmB+NAGgOngXm2oarZSikPez15dU4SVDH75BePKBOllCfwOfBXrfVFs+NxJFrrQq11ayAQ6KCUqvbDVUqpvkCa1jre7FgcWGetdRugN/C0bchTGJWR2wAztNbhwCXAbnPoqnOSkAwEXbUdCKSYFIuoRGxj7Z8Di7TWX5gdj6OydYnGAb1MDsURdAYesI27fwrco5T62NyQHIvWOsX2bxqwHGNIWBjfVclX9ch9hpE02EV1ThJ2AU2UUsG2iSCDgZUmxyQcnG1i3hzgoNZ6mtnxOBqlVIBSysd23Q3oDhwyNyrzaa0naK0DtdaNMD5r1muth5oclsNQSnnYJgJj60rvAciZVYDW+iTwm1Iq1LarG2C3idLVdoEnrXWBUuoZYA1gBeZqrQ+YHJbDUEotBqIAf6VUMvB3rfUcc6NyCJ2BYUCCbdwd4H+01qtMjMmR1AMW2M4esgBLtdZyup8oTR1guZGD4wR8orX+r7khOZRngUW2H7RJwOP2euJqewqkEEIIIW6uOg83CCGEEOImJEkQQgghRLEkSRBCCCFEsSRJEEIIIUSxJEkQQgghRLEkSRBCCCFEsSRJEEIIIUSxJEkQQgghRLEkSRBC2I1SKkQpla+UeuO6/TOUUhlKqXZmxSaEuJEkCUIIu9FaJwKzgeeVUv4ASqnXgJHAAK31bjPjE0JcS8oyCyHsSilVF/gF+A/G4k+zgEe01ktNDUwIcYNqu8CTTi+l3QAAAO1JREFUEMIcWuuTSqnpwAsYn0HjJEEQwjHJcIMQwgxHgBrANq31B2YHI4QoniQJQgi7UkrdA8wEtgGdlVKtTA5JCFECSRKEEHajlGoDfIkxeTEK+BV4y8yYhBAlkyRBCGEXSqkQYDXwLfCs1joPeAO4TykVaWpwQohiydkNQogKZzuj4XuMnoOeWutc234rsB84r7XuZGKIQohiSJIghBBCiGLJcIMQQgghiiVJghBCCCGKJUmCEEIIIYolSYIQQgghiiVJghBCCCGKJUmCEEIIIYolSYIQQgghiiVJghBCCCGKJUmCEEIIIYr1/wGrlhwm8BXecwAAAABJRU5ErkJggg==\n", 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Q1hyObYHv7peOjB4ktn6tvzoyfr+D3/emneMIIUy25DFIWQPBDWHIVLB6m52o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\nYII=\n", 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    " + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -278,7 +276,7 @@ "# adjust the limits of the horizontal axis\n", "ax.set_xlim(0., 2. * np.pi)\n", "# make a grid be displayed in the axis background\n", - "ax.grid(True)" + "ax.grid('on')" ] }, { @@ -306,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "nbpresent": { "id": "978c1095-a9ce-4626-a113-e0be5fe51ecb" @@ -315,40 +313,36 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJIAAACPCAYAAAARM4LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn2pIxAAAAABJRU5ErkJggg==\n", "text/plain": [ - "
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    " + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "Image target: [[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n" + "Image target: [[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n" ] } ], @@ -392,67 +386,57 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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ZF41tDuvr69G7d2+89dZb1oq0bFEBjUOnQ4cOoaKiQnVPtWDBAhPjU5GLbg4h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\nDj4+Pk26oFRWVsLFxcVE8OY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\n", 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ys7MBtF54MfOFJcPNFStWoKCgQBLR9u3bpXUauRfeGHv37uVamzlx4gRmzJhh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\nPQsG3e2tt96CVqtFbm6u1f4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\n", 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gQHFxMWpqarBixQr4+vraNAoWoaKiQv7ugIAAtGvXDu3atcOyZcsceUG0HlHN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\nphBvb2/h469cuUIIIWTq1Knk+++/J/n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ZsiXc3d2tJtBriLi4OMTFxYlsLTSP1b99+/bB19cXvr6+6o0/duyYrS9vJiRu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\n9xL1u6GUqoa8Tk5OuH//Pm7fvg1fX18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\n", 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02bls2TLF9dhLVlYW2rdvb7CaKGcpe+7cuSYbv61BYGCgyaoob2zFxsZGJCcn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\nxNOvXz+kp6ejtLQUBQUFiI6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\n", 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u5zrycvM4c+CZ0Q7NmHYL5+ypXwIHgSU4X8KnAcNU9d/DEWi4hWVMY3SGJQwT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\nYOMCXt31KvESz21jbmPWxFk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\n", 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JyOxyvrbptsQmc2cRPaI6NryyR9T49uPJycvho50fmRPU2UQ/BW7esKI8fTCq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\nvQ4e0EvSlyYnL5++/12Nl5s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\n", 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KgMOUBhjbb9NL00kvSbe/U8Zy8O0JUaMdI4SnHwyZDfsXQ7X9prJVaQUk9Q6g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\nCzqXAAAAAElFTkSuQmCC\n", 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    " + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -683,7 +661,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.2" } }, "nbformat": 4, diff --git a/notebooks/02_Single_layer_models.ipynb b/notebooks/02_Single_layer_models.ipynb index 35d0ae5..1070bba 100644 --- a/notebooks/02_Single_layer_models.ipynb +++ b/notebooks/02_Single_layer_models.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -92,14 +92,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.02 ms ± 27.3 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n" + "2.03 ms ± 19.4 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n" ] } ], @@ -119,15 +119,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The slowest run took 7.12 times longer than the fastest. This could mean that an intermediate result is being cached.\n", - "4.66 µs ± 4.38 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n" + "The slowest run took 8.59 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "5.51 µs ± 5.57 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n" ] } ], @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +188,7 @@ " Returns:\n", " outputs: Array of layer outputs of shape (batch_size, output_dim).\n", " \"\"\"\n", - " raise NotImplementedError('Delete this raise statement and write your code here instead.')" + " return inputs.dot(weights.T) + biases" ] }, { @@ -200,9 +200,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All outputs correct!\n" + ] + } + ], "source": [ "inputs = np.array([[0., -1., 2.], [-6., 3., 1.]])\n", "weights = np.array([[2., -3., -1.], [-5., 7., 2.]])\n", @@ -230,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -273,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -324,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -362,19 +370,800 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'fprop' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# Calculate predicted model outputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbiases\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# Plot target and predicted outputs against inputs on same axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'fprop' is not defined" - ] + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
    ');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
    ');\n", + " var titletext = $(\n", + " '
    ');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
    ');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
    ')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
    ');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Text(0,0.5,'Error')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from mlp.layers import AffineLayer\n", "from mlp.errors import SumOfSquaredDiffsError\n", @@ -661,7 +2297,7 @@ "\n", "# Run the optimiser for 5 epochs (full passes through the training set)\n", "# printing statistics every epoch.\n", - "stats, keys = optimiser.train(num_epochs=10, stats_interval=1)\n", + "stats, keys, _ = optimiser.train(num_epochs=10, stats_interval=1)\n", "\n", "# Plot the change in the error over training.\n", "fig = plt.figure(figsize=(8, 4))\n", @@ -680,9 +2316,802 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
    ');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
    ');\n", + " var titletext = $(\n", + " '
    ');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
    ');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
    ')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('

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