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models.py
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models.py
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import os
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from keras.applications.mobilenet import MobileNet, relu6, DepthwiseConv2D
from keras.applications.inception_v3 import InceptionV3
from keras.models import Model, load_model
from keras.layers import Input, Flatten, Dense, Dropout, BatchNormalization, GlobalAveragePooling2D
from keras.optimizers import SGD
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.utils.generic_utils import CustomObjectScope
from utils import NeptuneMonitor
class BasicKerasClassifier(BaseEstimator, ClassifierMixin):
def __init__(self,
architecture_cfg,
training_cfg,
callbacks_cfg):
self.architecture_cfg = architecture_cfg
self.training_cfg = training_cfg
self.callbacks_cfg = callbacks_cfg
self.model = self._build_model(**self.architecture_cfg)
self.callbacks = self._create_callbacks(**self.callbacks_cfg)
def fit(self, X, y=None, **kwargs):
train_flow, train_steps = X['X']
valid_flow, valid_steps = X['valid']
self.model.fit_generator(train_flow,
steps_per_epoch=train_steps,
validation_data=valid_flow,
validation_steps=valid_steps,
callbacks=self.callbacks,
**self.training_cfg)
self.model = self._load_best_model(self.model_filepath)
return self
def predict(self, X, **kwargs):
self.model = self._load_best_model(self.model_filepath)
test_flow, test_steps = X['X']
predictions = self.model.predict_generator(test_flow, test_steps, verbose=1)
return self._format_predictions(predictions)
def reset(self):
self.model = self._build_model(**self.architecture_config)
def _build_model(self, **kwargs):
return NotImplementedError
def _load_best_model(self, filepath):
return load_model(filepath)
def _create_callbacks(self, **kwargs):
self.model_filepath = ''
return NotImplementedError
def _format_predictions(self, predictions, **kwargs):
return NotImplementedError
class KerasInception(BasicKerasClassifier):
def _build_model(self, input_size, classes, trainable_threshold):
base_model = self._load_pretrained_model(input_size)
for i, layer in enumerate(base_model.layers):
if i < trainable_threshold:
layer.trainable = False
else:
layer.trainable = True
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(classes, activation='softmax', name='output')(x)
model = Model(inputs=base_model.input, outputs=predictions)
sgd = SGD(lr=0.001, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['acc'])
return model
def _load_pretrained_model(self, input_size, **kwargs):
return InceptionV3(include_top=False, weights='imagenet', input_shape=(input_size, input_size, 3))
def _load_best_model(self, filepath):
return load_model(filepath)
def _create_callbacks(self, models_dir, model_name):
self.model_filepath = os.path.join(models_dir, '{}.h5'.format(model_name))
model_checkpoint = ModelCheckpoint(self.model_filepath, monitor='val_loss', save_best_only=True)
neptune = NeptuneMonitor(model_name)
return [model_checkpoint, neptune]
def _format_predictions(self, predictions):
return predictions
class KerasMobileNet(BasicKerasClassifier):
def _build_model(self, input_size, classes):
base_model = self._load_pretrained_model(input_size)
for layer in base_model.layers:
layer.trainable = True
x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu', name='fc1')(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
predictions = Dense(classes, activation='softmax', name='output')(x)
model = Model(inputs=base_model.input, outputs=predictions)
sgd = SGD(lr=0.001, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['acc'])
return model
def _load_pretrained_model(self, input_size, **kwargs):
return MobileNet(weights='imagenet', include_top=False, input_shape=(input_size, input_size, 3))
def _load_best_model(self, filepath):
return load_mobilenets_model(filepath)
def _create_callbacks(self, models_dir, model_name):
self.model_filepath = os.path.join(models_dir, '{}.h5'.format(model_name))
model_checkpoint = ModelCheckpoint(self.model_filepath, monitor='val_loss', save_best_only=True)
neptune = NeptuneMonitor(model_name)
return [model_checkpoint, neptune]
def _format_predictions(self, predictions):
return predictions
def load_mobilenets_model(filepath):
with CustomObjectScope({'relu6': relu6, 'DepthwiseConv2D': DepthwiseConv2D}):
model = load_model(filepath)
return model