Building a CNN

In this notebook we'll rebuild the network presented in this paper called LeNet developed by Yann LeCun.

Using tensorflow's built-in tutorial data, we'll do some preprocessing first, then define the network layers and ultimately train and test a Convolutional Neural Network.



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import numpy as np
import tensorflow as tf
from sklearn.utils import shuffle
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.layers import flatten



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mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train           = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test             = mnist.test.images, mnist.test.labels

assert(len(X_train) == len(y_train))
assert(len(X_validation) == len(y_validation))
assert(len(X_test) == len(y_test))

print()
print("Image Shape: {}".format(X_train[0].shape))
print()
print("Training Set:   {} samples".format(len(X_train)))
print("Validation Set: {} samples".format(len(X_validation)))
print("Test Set:       {} samples".format(len(X_test)))

The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.

However, the LeNet architecture only accepts 32x32xC images, where C is the number of color channels.

In order to reformat the MNIST data into a shape that LeNet will accept, we pad the data with two rows of zeros on the top and bottom, and two columns of zeros on the left and right (28+2+2 = 32).



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# basic preprocessing
# padding images to match expected input
X_train      = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_test       = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')

# shuffling data
X_train, y_train = shuffle(X_train, y_train)

Implementing LeNet-5

Implement the LeNet-5 neural network architecture.

Input

The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.

Architecture

Layer 1: Convolutional. The output shape should be 28x28x6.

Activation. Your choice of activation function.

Pooling. The output shape should be 14x14x6.

Layer 2: Convolutional. The output shape should be 10x10x16.

Activation. Your choice of activation function.

Pooling. The output shape should be 5x5x16.

Flatten. Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using tf.contrib.layers.flatten, which is already imported for you.

Layer 3: Fully Connected. This should have 120 outputs.

Activation. Your choice of activation function.

Layer 4: Fully Connected. This should have 84 outputs.

Activation. Your choice of activation function.

Layer 5: Fully Connected (Logits). This should have 10 outputs.

Output

Return the result of the 2nd fully connected layer.



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def LeNet(x):    
    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
    mu = 0
    sigma = 0.1
    
    # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.

    # TODO: Activation.

    # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.

    # TODO: Layer 2: Convolutional. Output = 10x10x16.
    
    # TODO: Activation.

    # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.

    # TODO: Flatten. Input = 5x5x16. Output = 400.
    
    # TODO: Layer 3: Fully Connected. Input = 400. Output = 120.
    
    # TODO: Activation.

    # TODO: Layer 4: Fully Connected. Input = 120. Output = 84.
    
    # TODO: Activation.

    # TODO: Layer 5: Fully Connected. Input = 84. Output = 10.
    
    return logits

Training pipeline



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x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 10)


learning_rate = 0.001
epochs = 10
batch_size = 128

# the model you defined
logits = LeNet(x)

# define the evaluation functions
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)

loss_operation = tf.reduce_mean(cross_entropy)

optimizer = tf.train.AdamOptimizer(learning_rate=learning_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})
        total_accuracy += (accuracy * len(batch_x))
    return total_accuracy / num_examples

Training the model

Run the training data through the training pipeline to train the model. Before each epoch, shuffle the training set. After each epoch, measure the loss and accuracy of the validation set. Save the model after training. You do not need to modify this section.



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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})
            
        validation_accuracy = evaluate(X_validation, y_validation)
        print("EPOCH {} ...".format(i+1))
        print("Validation Accuracy = {:.3f}".format(validation_accuracy))
        print()
        
    saver.save(sess, './lenet')
    print("Model saved")

Evaluation of the model

Once you are completely satisfied with your model, evaluate the performance of the model on the test set.



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with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    test_accuracy = evaluate(X_test, y_test)
    print("Test Accuracy = {:.3f}".format(test_accuracy))

potential solution


    # Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
    conv1_b = tf.Variable(tf.zeros(6))
    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b

    # Activation.
    conv1 = tf.nn.relu(conv1)

    # Pooling. Input = 28x28x6. Output = 14x14x6.
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    # Layer 2: Convolutional. Output = 10x10x16.
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
    conv2_b = tf.Variable(tf.zeros(16))
    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b

    # Activation.
    conv2 = tf.nn.relu(conv2)

    # Pooling. Input = 10x10x16. Output = 5x5x16.
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    # Flatten. Input = 5x5x16. Output = 400.
    fc0   = flatten(conv2)

    # Layer 3: Fully Connected. Input = 400. Output = 120.
    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
    fc1_b = tf.Variable(tf.zeros(120))
    fc1   = tf.matmul(fc0, fc1_W) + fc1_b

    # Activation.
    fc1    = tf.nn.relu(fc1)

    # Layer 4: Fully Connected. Input = 120. Output = 84.
    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
    fc2_b  = tf.Variable(tf.zeros(84))
    fc2    = tf.matmul(fc1, fc2_W) + fc2_b

    # Activation.
    fc2    = tf.nn.relu(fc2)

    # Layer 5: Fully Connected. Input = 84. Output = 10.
    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 10), mean = mu, stddev = sigma))
    fc3_b  = tf.Variable(tf.zeros(10))
    logits = tf.matmul(fc2, fc3_W) + fc3_b



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