diff --git a/CarND-Traffic-Sign-Classifier-resubmit.ipynb b/CarND-Traffic-Sign-Classifier-resubmit.ipynb.old similarity index 100% rename from CarND-Traffic-Sign-Classifier-resubmit.ipynb rename to CarND-Traffic-Sign-Classifier-resubmit.ipynb.old diff --git a/CarND_Traffic_Sign_Classifier.html b/CarND_Traffic_Sign_Classifier.html new file mode 100644 index 0000000000..d9eaa447e0 --- /dev/null +++ b/CarND_Traffic_Sign_Classifier.html @@ -0,0 +1,15732 @@ + + +
+ +In this notebook, a template is provided for you to implement your functionality in stages which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission, if necessary. Sections that begin with 'Implementation' in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with 'Optional' in the header.
+In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
++ +Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
+
# Load pickled data
+import pickle
+
+# TODO: Fill this in based on where you saved the training and testing data
+
+training_file = "./traffic-signs-data/train.p"
+testing_file = "./traffic-signs-data/test.p"
+
+with open(training_file, mode='rb') as f:
+ train = pickle.load(f)
+with open(testing_file, mode='rb') as f:
+ test = pickle.load(f)
+
+X_train, y_train = train['features'], train['labels']
+X_test, y_test = test['features'], test['labels']
+
+print("X_train shape:", X_train.shape)
+print("y_train shape:", y_train.shape)
+print("X_test shape:", X_test.shape)
+print("y_test shape:", y_test.shape)
+
The pickled data is a dictionary with 4 key/value pairs:
+'features'
is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).'labels'
is a 2D array containing the label/class id of the traffic sign. The file signnames.csv
contains id -> name mappings for each id.'sizes'
is a list containing tuples, (width, height) representing the the original width and height the image.'coords'
is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGESComplete the basic data summary below.
+ +### Replace each question mark with the appropriate value.
+import numpy as np
+
+# TODO: Number of training examples
+n_train = len(X_train)
+
+# TODO: Number of testing examples.
+n_test = len(X_test)
+
+# TODO: What's the shape of an traffic sign image?
+image_shape = X_train[0].shape
+
+# TODO: How many unique classes/labels there are in the dataset.
+n_classes = len(np.unique(y_train))
+
+print("Number of training examples =", n_train)
+print("Number of testing examples =", n_test)
+print("Image data shape =", image_shape)
+print("Number of classes =", n_classes)
+
Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.
+The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.
+NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections.
+ +### Data exploration visualization goes here.
+### Feel free to use as many code cells as needed.
+import matplotlib.pyplot as plt
+import random
+# Visualizations will be shown in the notebook.
+%matplotlib inline
+
+# show image of 10 random data points
+fig, axs = plt.subplots(2,5, figsize=(15, 6))
+fig.subplots_adjust(hspace = .2, wspace=.001)
+axs = axs.ravel()
+for i in range(10):
+ index = random.randint(0, len(X_train))
+ image = X_train[index]
+ axs[i].axis('off')
+ axs[i].imshow(image)
+ axs[i].set_title(y_train[index])
+
# histogram of label frequency
+hist, bins = np.histogram(y_train, bins=n_classes)
+width = 0.7 * (bins[1] - bins[0])
+center = (bins[:-1] + bins[1:]) / 2
+plt.bar(center, hist, align='center', width=width)
+plt.show()
+
Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.
+There are various aspects to consider when thinking about this problem:
+Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.
+NOTE: The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!
+ +Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.
+### Preprocess the data here.
+### Feel free to use as many code cells as needed.
+
+# Convert to grayscale
+X_train_rgb = X_train
+X_train_gry = np.sum(X_train/3, axis=3, keepdims=True)
+
+X_test_rgb = X_test
+X_test_gry = np.sum(X_test/3, axis=3, keepdims=True)
+
+print('RGB dataset shape:', X_train_rgb.shape)
+print('Grayscale dataset shape:', X_train_gry.shape)
+
X_train = X_train_gry
+X_test = X_test_gry
+
+print('Training and test datasets processed - done')
+
# Visualize rgb vs grayscale
+n_rows = 8
+n_cols = 10
+offset = 9000
+fig, axs = plt.subplots(n_rows,n_cols, figsize=(18, 14))
+fig.subplots_adjust(hspace = .1, wspace=.001)
+axs = axs.ravel()
+for j in range(0,n_rows,2):
+ for i in range(n_cols):
+ index = i + j*n_cols
+ image = X_train_rgb[index + offset]
+ axs[index].axis('off')
+ axs[index].imshow(image)
+ for i in range(n_cols):
+ index = i + j*n_cols + n_cols
+ image = X_train_gry[index + offset - n_cols].squeeze()
+ axs[index].axis('off')
+ axs[index].imshow(image, cmap='gray')
+
print(y_train[0:500])
+
print(np.mean(X_train))
+print(np.mean(X_test))
+
## Normalize the train and test datasets to (-1,1)
+
+X_train_normalized = (X_train - 128)/128
+X_test_normalized = (X_test - 128)/128
+
+print(np.mean(X_train_normalized))
+print(np.mean(X_test_normalized))
+
print("Original shape:", X_train.shape)
+print("Normalized shape:", X_train_normalized.shape)
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+axs = axs.ravel()
+
+axs[0].axis('off')
+axs[0].set_title('normalized')
+axs[0].imshow(X_train_normalized[0].squeeze(), cmap='gray')
+
+axs[1].axis('off')
+axs[1].set_title('original')
+axs[1].imshow(X_train[0].squeeze(), cmap='gray')
+
Answer:
+My dataset preprocessing consisted of:
+As a first step, I decided to convert the images to grayscale because, the neural network would be very hard to train in color. The RGB image would have 3 channels; ie n x n x 3 however, a grayscale would be n x n x 1.
+As an example, set the n to 3 and output to 64, an RGB image would have 1728 parameters and the grayscale would have 576 parameters in the first layer.
+Here is an example of a traffic sign image before and after grayscaling.
+![Augmented-images-normalized][./normalize.png] +![Augmented-images-translated][./translate.png] +![Augmented-images-scaled][./scaling.png] +![Augmented-images-warped][./warp.png] +![Augmented-images-brightness-adjusted][./brightness.png]
+ +### Generate data additional data (OPTIONAL!)
+### and split the data into training/validation/testing sets here.
+### Feel free to use as many code cells as needed.
+
I used the following four functions for augmenting the dataset:
+import cv2
+
+def random_translate(img):
+ rows,cols,_ = img.shape
+
+ # allow translation up to px pixels in x and y directions
+ px = 2
+ dx,dy = np.random.randint(-px,px,2)
+
+ M = np.float32([[1,0,dx],[0,1,dy]])
+ dst = cv2.warpAffine(img,M,(cols,rows))
+
+ dst = dst[:,:,np.newaxis]
+
+ return dst
+
+test_img = X_train_normalized[22222]
+
+test_dst = random_translate(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('translated')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
def random_scaling(img):
+ rows,cols,_ = img.shape
+
+ # transform limits
+ px = np.random.randint(-2,2)
+
+ # ending locations
+ pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]])
+
+ # starting locations (4 corners)
+ pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]])
+
+ M = cv2.getPerspectiveTransform(pts1,pts2)
+
+ dst = cv2.warpPerspective(img,M,(rows,cols))
+
+ dst = dst[:,:,np.newaxis]
+
+ return dst
+
+test_dst = random_scaling(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('scaled')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
def random_warp(img):
+
+ rows,cols,_ = img.shape
+
+ # random scaling coefficients
+ rndx = np.random.rand(3) - 0.5
+ rndx *= cols * 0.06 # this coefficient determines the degree of warping
+ rndy = np.random.rand(3) - 0.5
+ rndy *= rows * 0.06
+
+ # 3 starting points for transform, 1/4 way from edges
+ x1 = cols/4
+ x2 = 3*cols/4
+ y1 = rows/4
+ y2 = 3*rows/4
+
+ pts1 = np.float32([[y1,x1],
+ [y2,x1],
+ [y1,x2]])
+ pts2 = np.float32([[y1+rndy[0],x1+rndx[0]],
+ [y2+rndy[1],x1+rndx[1]],
+ [y1+rndy[2],x2+rndx[2]]])
+
+ M = cv2.getAffineTransform(pts1,pts2)
+
+ dst = cv2.warpAffine(img,M,(cols,rows))
+
+ dst = dst[:,:,np.newaxis]
+
+ return dst
+
+test_dst = random_warp(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('warped')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
def random_brightness(img):
+ shifted = img + 1.0 # shift to (0,2) range
+ img_max_value = max(shifted.flatten())
+ max_coef = 2.0/img_max_value
+ min_coef = max_coef - 0.1
+ coef = np.random.uniform(min_coef, max_coef)
+ dst = shifted * coef - 1.0
+ return dst
+
+test_dst = random_brightness(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('brightness adjusted')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
# histogram of label frequency (once again, before data augmentation)
+hist, bins = np.histogram(y_train, bins=n_classes)
+width = 0.7 * (bins[1] - bins[0])
+center = (bins[:-1] + bins[1:]) / 2
+plt.bar(center, hist, align='center', width=width)
+plt.show()
+
print(np.bincount(y_train))
+print("minimum samples for any label:", min(np.bincount(y_train)))
+
print('X, y shapes:', X_train_normalized.shape, y_train.shape)
+
+input_indices = []
+output_indices = []
+
+for class_n in range(n_classes):
+ print(class_n, ': ', end='')
+ class_indices = np.where(y_train == class_n)
+ n_samples = len(class_indices[0])
+ if n_samples < 800:
+ for i in range(800 - n_samples):
+ input_indices.append(class_indices[0][i%n_samples])
+ output_indices.append(X_train_normalized.shape[0])
+ new_img = X_train_normalized[class_indices[0][i % n_samples]]
+ new_img = random_translate(random_scaling(random_warp(random_brightness(new_img))))
+ X_train_normalized = np.concatenate((X_train_normalized, [new_img]), axis=0)
+ y_train = np.concatenate((y_train, [class_n]), axis=0)
+ if i % 50 == 0:
+ print('>', end='')
+ elif i % 10 == 0:
+ print('-',end='')
+ print('')
+
+print('X, y shapes:', X_train_normalized.shape, y_train.shape)
+
+
# show comparisons of data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABKAAAAINCAYAAAAJCwe2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 random augmented data points
+choices = list(range(len(input_indices)))
+picks = []
+for i in range(5):
+ rnd_index = np.random.randint(low=0,high=len(choices))
+ picks.append(choices.pop(rnd_index))
+fig, axs = plt.subplots(2,5, figsize=(15, 6))
+fig.subplots_adjust(hspace = .2, wspace=.001)
+axs = axs.ravel()
+for i in range(5):
+ image = X_train_normalized[input_indices[picks[i]]].squeeze()
+ axs[i].axis('off')
+ axs[i].imshow(image, cmap = 'gray')
+ axs[i].set_title(y_train[input_indices[picks[i]]])
+for i in range(5):
+ image = X_train_normalized[output_indices[picks[i]]].squeeze()
+ axs[i+5].axis('off')
+ axs[i+5].imshow(image, cmap = 'gray')
+ axs[i+5].set_title(y_train[output_indices[picks[i]]])
+
# histogram of label frequency
+hist, bins = np.histogram(y_train, bins=n_classes)
+width = 0.7 * (bins[1] - bins[0])
+center = (bins[:-1] + bins[1:]) / 2
+plt.bar(center, hist, align='center', width=width)
+plt.show()
+
## Shuffle the training dataset
+
+from sklearn.utils import shuffle
+
+X_train_normalized, y_train = shuffle(X_train_normalized, y_train)
+
+print('done')
+
## Split validation dataset off from training dataset
+
+from sklearn.model_selection import train_test_split
+
+X_train, X_validation, y_train, y_validation = train_test_split(X_train_normalized, y_train,
+ test_size=0.20, random_state=42)
+
+print("Old X_train size:",len(X_train_normalized))
+print("New X_train size:",len(X_train))
+print("X_validation size:",len(X_validation))
+
Layer | +Description | +
---|---|
Input | +32x32x3 RGB image | +
Layer 1 Convolution 3x3 | +Input = 32x32ximage_depth. Output = 28x28x6 | +
RELU | ++ |
Max pooling | +Input = 28x28x6. Output = 14x14x6 | +
Layer 2 Convolution 3x3 | +Output = 10x10x16 | +
RELU | ++ |
Max pooling | +Input = 10x10x16. Output = 5x5x16 | +
Layer 3 Fully connected | +Fully Connected. Input = 400. Output = 120 | +
RELU | ++ |
Layer 4 Fully connected | +Fully Connected. Input = 120. Output = 84 | +
RELU | ++ |
Layer 5 Fully connected | +Fully Connected. Input = 84. Output = 43 | +
logits | +Finalize and return the logits | +
With the original dataset not giving optimum results, I +decided to perform data augmentation as it is know to increase accuracy of the model.
+On observation we can see that several classes in the data have far fewer samples than others the model will tend to be biased toward those classes with more samples.
+Useful python module SciKit Learn train_test_split function was used to create a validation set out of the training set. I used 20% of the testing set to create the validation set.
+Initially to train the model, I used default LeNet model as discussed in the class and that comprises of the layers given in the above table. The number of EPOCHs were 10. The learning rates tried were 0.1 through 0.05 and I got horrible accuracies of under 90% !!
+Then I updated the learning rate to 0.0009 and it seemed to give the highest accuracy > 99%, while still not slowing down the prcessing a lot.
+The following is the summary:
+Adam optimizer was used as part of the LeNet lab. The final settings used were:
+As far as a discussion on the difficulty in classification, the following are notable
+import tensorflow as tf
+
+EPOCHS = 60
+BATCH_SIZE = 100
+
+print('done')
+
#from tensorflow.contrib.layers import flatten
+import tensorflow
+from tensorflow.keras.layers import Flatten as flatten
+
+def LeNet(x):
+ # Hyperparameters
+ mu = 0
+ sigma = 0.1
+
+ # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
+ W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
+ x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')
+ b1 = tf.Variable(tf.zeros(6))
+ x = tf.nn.bias_add(x, b1)
+ print("layer 1 shape:",x.get_shape())
+
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+
+ # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.
+ x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+
+ # TODO: Layer 2: Convolutional. Output = 10x10x16.
+ W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
+ x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')
+ b2 = tf.Variable(tf.zeros(16))
+ x = tf.nn.bias_add(x, b2)
+
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+
+ # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.
+ x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+
+ # TODO: Flatten. Input = 5x5x16. Output = 400.
+ x = flatten(x)
+
+ # TODO: Layer 3: Fully Connected. Input = 400. Output = 120.
+ W3 = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
+ b3 = tf.Variable(tf.zeros(120))
+ x = tf.add(tf.matmul(x, W3), b3)
+
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+
+ # Dropout
+ x = tf.nn.dropout(x, keep_prob)
+
+ # TODO: Layer 4: Fully Connected. Input = 120. Output = 84.
+ W4 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
+ b4 = tf.Variable(tf.zeros(84))
+ x = tf.add(tf.matmul(x, W4), b4)
+
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+
+ # Dropout
+ x = tf.nn.dropout(x, keep_prob)
+
+ # TODO: Layer 5: Fully Connected. Input = 84. Output = 43.
+ W5 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
+ b5 = tf.Variable(tf.zeros(43))
+ logits = tf.add(tf.matmul(x, W5), b5)
+
+ return logits
+
+print('LeNet5 Classic done')
+
The achitecture has been adapted from Sermanet/LeCunn traffic sign classification journal article. Please refer to the article for more information.
+Modified LeCun5 architecture +
+ +#from tensorflow.contrib.layers import flatten
+import tensorflow
+from tensorflow.keras.layers import Flatten as flatten
+
+
+def LeNet5_updated(x):
+ # Hyperparameters
+ mu = 0
+ sigma = 0.1
+
+ # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
+ W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma), name="W1")
+ x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')
+ b1 = tf.Variable(tf.zeros(6), name="b1")
+ x = tf.nn.bias_add(x, b1)
+ print("layer 1 shape:",x.get_shape())
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+ # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.
+ x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+ layer1 = x
+
+ # TODO: Layer 2: Convolutional. Output = 10x10x16.
+ W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma), name="W2")
+ x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')
+ b2 = tf.Variable(tf.zeros(16), name="b2")
+ x = tf.nn.bias_add(x, b2)
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+ # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.
+ x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+ layer2 = x
+
+ # TODO: Layer 3: Convolutional. Output = 1x1x400.
+ W3 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 400), mean = mu, stddev = sigma), name="W3")
+ x = tf.nn.conv2d(x, W3, strides=[1, 1, 1, 1], padding='VALID')
+ b3 = tf.Variable(tf.zeros(400), name="b3")
+ x = tf.nn.bias_add(x, b3)
+ # TODO: Activation.
+ x = tf.nn.relu(x)
+ layer3 = x
+ # TODO: Flatten. Input = 5x5x16. Output = 400.
+ #layer2flat = flatten(layer2)
+ layer2flat = tensorflow.reshape(layer2, [tensorflow.shape(layer2)[0], -1])
+ print("layer2flat shape:",layer2flat.get_shape())
+ # Flatten x. Input = 1x1x400. Output = 400.
+ #xflat = flatten(x)
+ xflat = flatten()(x)
+ print("xflat shape:",xflat.get_shape())
+ # Concat layer2flat and x. Input = 400 + 400. Output = 800
+ #x = tf.concat_v2([xflat, layer2flat], 1)
+ x = tf.concat([xflat, layer2flat], 1)
+ print("x shape:",x.get_shape())
+ # Dropout
+ x = tf.nn.dropout(x, keep_prob)
+
+ # TODO: Layer 4: Fully Connected. Input = 800. Output = 43.
+ W4 = tf.Variable(tf.truncated_normal(shape=(800, 43), mean = mu, stddev = sigma), name="W4")
+ b4 = tf.Variable(tf.zeros(43), name="b4")
+ logits = tf.add(tf.matmul(x, W4), b4)
+
+
+ return logits
+
+print('LeNet5 Modified done')
+
tf.reset_default_graph()
+
+x = tf.placeholder(tf.float32, (None, 32, 32, 1))
+y = tf.placeholder(tf.int32, (None))
+keep_prob = tf.placeholder(tf.float32) # probability to keep units
+one_hot_y = tf.one_hot(y, 43)
+
+print('done')
+
To train the model, I used LeNet that comprises of the layers given in the above table. I began by implementing the same architecture from the LeNet Lab, with no changes since my dataset is in grayscale. This model worked quite well to begin with (> 95% validation accuracy), but I also implemented the Sermanet/LeCun model from their traffic sign classifier paper and saw an immediate improvement. Although the paper doesn't go into detail describing exactly how the model is implemented (particularly the depth of the layers)
+The updated model will be as follows:
+### Train your model here.
+### Feel free to use as many code cells as needed.
+
rate = 0.0009
+
+logits = LeNet5_updated(x)
+#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)
+with tf.name_scope('loss'):
+ #cross_entropy = None
+ val = tf.nn.softmax_cross_entropy_with_logits(labels = one_hot_y, logits=logits)
+ cross_entropy = tf.reduce_mean(val)
+loss_operation = tf.reduce_mean(cross_entropy)
+optimizer = tf.train.AdamOptimizer(learning_rate = rate)
+training_operation = optimizer.minimize(loss_operation)
+
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
+accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+saver = tf.train.Saver()
+
+def evaluate(X_data, y_data):
+ num_examples = len(X_data)
+ total_accuracy = 0
+ sess = tf.get_default_session()
+ for offset in range(0, num_examples, BATCH_SIZE):
+ batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
+ accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
+ total_accuracy += (accuracy * len(batch_x))
+ return total_accuracy / num_examples
+
+print('done')
+
with tf.Session() as sess:
+ sess.run(tf.global_variables_initializer())
+ num_examples = len(X_train)
+
+ print("Training...")
+ print()
+ for i in range(EPOCHS):
+ X_train, y_train = shuffle(X_train, y_train)
+ for offset in range(0, num_examples, BATCH_SIZE):
+ end = offset + BATCH_SIZE
+ batch_x, batch_y = X_train[offset:end], y_train[offset:end]
+ sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})
+
+ validation_accuracy = evaluate(X_validation, y_validation)
+ print("EPOCH {} ...".format(i+1))
+ print("Validation Accuracy = {:.3f}".format(validation_accuracy))
+ print()
+
+ saver.save(sess, './traffic_signs')
+ print("Model saved")
+
with tf.Session() as sess:
+ sess.run(tf.global_variables_initializer())
+ saver2 = tf.train.import_meta_graph("./traffic_signs.meta")
+ saver2.restore(sess, "./traffic_signs")
+ test_accuracy = evaluate(X_test_normalized, y_test)
+ print("Test Set Accuracy = {:.3f}".format(test_accuracy))
+
In my approach, I split the data into training data, test data and then validation data based on the provided pickled data and also experimented with scikit module's train_test_split function. I will continue to experiment this function. Data augmentation as learnt from the course and researched on the internet was a useful technique for better accuracy. I
+The following are the model results. I was able to achieve the test data accuracy of > 0.93 or 93% by tweeking the learning rate, adding the layers and updating the connectedness of the layers.
+If an iterative approach was chosen:
+I downloaded several pictures of the german traffic dataset (at least five), and ran them through the classifier. The classifier gave only 12.5% accuracy. signnames.csv
useful as it contains mappings from the class id (integer) to the actual sign name.
Here are five German traffic signs that I found on the web:
+![Image 1][./traffic-signs-data/online_files/1.jpg] +![Image 2][./traffic-signs-data/online_files/2.jpg] +![Image 3][./traffic-signs-data/online_files/3.jpg] +![Image 4][./traffic-signs-data/online_files/4.jpg] +![Image 5][./traffic-signs-data/online_files/5.jpg]
+ +Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.
+ +# Reinitialize and re-import if starting a new kernel here
+import matplotlib.pyplot as plt
+%matplotlib inline
+
+import tensorflow as tf
+import numpy as np
+import cv2
+
+print('done')
+
### Load the images and plot them here.
+### Feel free to use as many code cells as needed.
+
+#reading in an image
+import glob
+import matplotlib.image as mpimg
+
+fig, axs = plt.subplots(2,4, figsize=(4, 2))
+fig.subplots_adjust(hspace = .2, wspace=.001)
+axs = axs.ravel()
+
+my_images = []
+
+for i, img in enumerate(glob.glob('./my-found-traffic-signs/*x.png')):
+#for i, img in enumerate(glob.glob('./traffic-signs-data/online-files/*.jpg')):
+ image = cv2.imread(img)
+ axs[i].axis('off')
+ axs[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+ my_images.append(image)
+
+my_images = np.asarray(my_images)
+
+my_images_gry = np.sum(my_images/3, axis=3, keepdims=True)
+
+my_images_normalized = (my_images_gry - 128)/128
+
+print(my_images_normalized.shape)
+
The classification was as expected, when an image was very different from my local or the downloaded online image, the system had an accuracy of around 12.5%
+But when I used familiar traffic sign images, these images seem to be distinguishable easier than than quite a few images from the original dataset.
+Some of the my images seem to be much brighter and might occupy a different range in the color space, possibly a range that the model was not trained on.
+In addition, the German dataset states that the images "contain a border of 10 % around the actual traffic sign (at least 5 pixels) to allow for edge-based approaches" and the images that I used do not all include such a border. This could be another source of confusion for the model.
+ +### Run the predictions here.
+### Feel free to use as many code cells as needed.
+
+my_labels = [3, 11, 1, 12, 38, 34, 18, 25]
+#my_labels = [3, 11, 1, 12]
+#my_labels = [14, 1, 25, 9, 5]
+
+
+with tf.Session() as sess:
+ sess.run(tf.global_variables_initializer())
+ saver3 = tf.train.import_meta_graph('./traffic_signs.meta')
+ saver3.restore(sess, "./traffic_signs")
+ my_accuracy = evaluate(my_images_normalized, my_labels)
+ print("Test Set Accuracy = {:.3f}".format(my_accuracy))
+
The model appears to have predicted the new but similar signs perfectly, with 100% accuracy - even better than the 99.3% validation accuracy and the 94.7% test accuracy. It is a good sign that the model performs well on real-world data.
+However, it is reasonable to assume that the accuracy would not remain so high given more data points, the low fidelity of a number of images in the training dataset can also be a reasonable explanation to assume that if the real-world data were all as easily distinguishable as the images chosen that the accuracy would remain very high.
+ +### Visualize the softmax probabilities here.
+### Feel free to use as many code cells as needed.
+
+softmax_logits = tf.nn.softmax(logits)
+top_k = tf.nn.top_k(softmax_logits, k=3)
+
+
+with tf.Session() as sess:
+ sess.run(tf.global_variables_initializer())
+ saver = tf.train.import_meta_graph('./traffic_signs.meta')
+ saver.restore(sess, "./traffic_signs")
+ my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_images_normalized, keep_prob: 1.0})
+ my_top_k = sess.run(top_k, feed_dict={x: my_images_normalized, keep_prob: 1.0})
+
+
+ fig, axs = plt.subplots(len(my_images),4, figsize=(12, 14))
+ fig.subplots_adjust(hspace = .4, wspace=.2)
+ axs = axs.ravel()
+
+ for i, image in enumerate(my_images):
+ axs[4*i].axis('off')
+ axs[4*i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+ axs[4*i].set_title('input')
+ guess1 = my_top_k[1][i][0]
+ index1 = np.argwhere(y_validation == guess1)[0]
+ axs[4*i+1].axis('off')
+ axs[4*i+1].imshow(X_validation[index1].squeeze(), cmap='gray')
+ axs[4*i+1].set_title('top guess: {} ({:.0f}%)'.format(guess1, 100*my_top_k[0][i][0]))
+ guess2 = my_top_k[1][i][1]
+ index2 = np.argwhere(y_validation == guess2)[0]
+ axs[4*i+2].axis('off')
+ axs[4*i+2].imshow(X_validation[index2].squeeze(), cmap='gray')
+ axs[4*i+2].set_title('2nd guess: {} ({:.0f}%)'.format(guess2, 100*my_top_k[0][i][1]))
+ guess3 = my_top_k[1][i][2]
+ index3 = np.argwhere(y_validation == guess3)[0]
+ axs[4*i+3].axis('off')
+ axs[4*i+3].imshow(X_validation[index3].squeeze(), cmap='gray')
+ axs[4*i+3].set_title('3rd guess: {} ({:.0f}%)'.format(guess3, 100*my_top_k[0][i][2]))
+
Use the model's softmax probabilities to visualize the certainty of its predictions, tf.nn.top_k
could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)
tf.nn.top_k
will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.
Take this numpy array as an example:
+ +# (5, 6) array
+a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,
+ 0.12789202],
+ [ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,
+ 0.15899337],
+ [ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,
+ 0.23892179],
+ [ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,
+ 0.16505091],
+ [ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,
+ 0.09155967]])
+Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3))
produces:
TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],
+ [ 0.28086119, 0.27569815, 0.18063401],
+ [ 0.26076848, 0.23892179, 0.23664738],
+ [ 0.29198961, 0.26234032, 0.16505091],
+ [ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],
+ [0, 1, 4],
+ [0, 5, 1],
+ [1, 3, 5],
+ [1, 4, 3]], dtype=int32))
+Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202]
, you can confirm these are the 3 largest probabilities in a
. You'll also notice [3, 0, 5]
are the corresponding indices.
fig, axs = plt.subplots(8,2, figsize=(9, 19))
+axs = axs.ravel()
+
+for i in range(len(my_softmax_logits)*2):
+ if i%2 == 0:
+ axs[i].axis('off')
+ axs[i].imshow(cv2.cvtColor(my_images[i//2], cv2.COLOR_BGR2RGB))
+ else:
+ axs[i].bar(np.arange(n_classes), my_softmax_logits[(i-1)//2])
+ axs[i].set_ylabel('Softmax probability')
+
+
The well trained model seems to have a very high accuracy on the images given. Visualizing the images, this seems accurate . Even on the third image, it's 92% certain of its prediction.
+This very high level of certainty, along with achieving 100% accuracy, on the newly introduced real-world data is indicative of a model that performs very well.
+ ++ +Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", + "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
+
print("X_train shape:", X_train.shape)
+print("y_train shape:", y_train.shape)
+print("X_validation shape:", X_validation.shape)
+print("y_validation shape:", y_validation.shape)
+print("X_test shape:", X_test_normalized.shape)
+print("y_test shape:", y_test.shape)
+
+