In [1]:
from tensorflow.examples.tutorials.mnist import input_data
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).
You do not need to modify this section.
In [2]:
import numpy as np
# Pad images with 0s
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')
print("Updated Image Shape: {}".format(X_train[0].shape))
In [3]:
import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
index = random.randint(0, len(X_train))
image = X_train[index].squeeze()
plt.figure(figsize=(1,1))
plt.imshow(image, cmap="gray")
print(y_train[index])
In [4]:
from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train)
In [5]:
import tensorflow as tf
EPOCHS = 10
BATCH_SIZE = 128
Implement the LeNet-5 neural network architecture.
This is the only cell you need to edit.
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.
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.
Return the result of the 2nd fully connected layer.
In [6]:
from tensorflow.contrib.layers import flatten
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.
# new_height = (input_height - filter_height + 2 * P)/S + 1
# 28 = ((32 - fh)/S) + 1
#(27 * S) + fh= 32
# S = 1, fh = 5
F_W = tf.Variable(tf.truncated_normal((5, 5, 1, 6), dtype=tf.float32))
F_b = tf.Variable(tf.zeros(6))
strides = [1, 1, 1, 1]
padding = 'VALID'
layer1 = tf.nn.conv2d(x, F_W, strides, padding) + F_b
# TODO: Activation.
layer1 = tf.nn.relu(layer1)
# TODO: Pooling. Input = 28x28x6. Output = 14x14x6.
# new_height = (input_height - filter_height)/S + 1
# 14 = ((28 - fh)/S) + 1
# (13 * S) + fh = 28
# S = 2, fh = 2
ksize=[1, 2, 2, 1]
strides=[1, 2, 2, 1]
padding = 'VALID'
pooling = tf.nn.max_pool(layer1, ksize, strides, padding)
# TODO: Layer 2: Convolutional. Output = 10x10x16.
# new_height = (input_height - filter_height + 2 * P)/S + 1
# 10 = ((14 - fh)/S) + 1
# (9 * S) + fh = 14
# S = 1, fh = 5
F_W = tf.Variable(tf.truncated_normal((5, 5, 6, 16), dtype=tf.float32))
F_b = tf.Variable(tf.zeros(16))
strides = [1, 1, 1, 1]
padding = 'VALID'
layer2 = tf.nn.conv2d(pooling, F_W, strides, padding) + F_b
# TODO: Activation.
layer2 = tf.nn.relu(layer2)
# TODO: Pooling. Input = 10x10x16. Output = 5x5x16.
# new_height = (input_height - filter_height)/S + 1
# 5 = ((10 - fh)/S) + 1
# (4 * S) + fh = 10
# S = 2, fh = 2
ksize=[1, 2, 2, 1]
strides=[1, 2, 2, 1]
padding = 'VALID'
pooling = tf.nn.max_pool(layer2, ksize, strides, padding)
# TODO: Flatten. Input = 5x5x16. Output = 400.
flatten = tf.contrib.layers.flatten(pooling)
# TODO: Layer 3: Fully Connected. Input = 400. Output = 120.
F_W = tf.Variable(tf.truncated_normal((400, 120), dtype=tf.float32))
F_b = tf.Variable(tf.zeros(120))
fully_connected = tf.matmul(flatten, F_W) + F_b
# TODO: Activation.
fully_connected = tf.nn.relu(fully_connected)
# TODO: Layer 4: Fully Connected. Input = 120. Output = 84.
F_W = tf.Variable(tf.truncated_normal((120, 84), dtype=tf.float32))
F_b = tf.Variable(tf.zeros(84))
fully_connected = tf.matmul(fully_connected, F_W) + F_b
# TODO: Activation.
fully_connected = tf.nn.relu(fully_connected)
# TODO: Layer 5: Fully Connected. Input = 84. Output = 10.
F_W = tf.Variable(tf.truncated_normal((84, 10), dtype=tf.float32))
F_b = tf.Variable(tf.zeros(10))
logits = tf.matmul(fully_connected, F_W) + F_b
return logits
Train LeNet to classify MNIST data.
x is a placeholder for a batch of input images.
y is a placeholder for a batch of output labels.
You do not need to modify this section.
In [7]:
x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 10)
In [8]:
rate = 0.001
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
In [9]:
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
In [10]:
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")
Once you are completely satisfied with your model, evaluate the performance of the model on the test set.
Be sure to only do this once!
If you were to measure the performance of your trained model on the test set, then improve your model, and then measure the performance of your model on the test set again, that would invalidate your test results. You wouldn't get a true measure of how well your model would perform against real data.
You do not need to modify this section.
In [12]:
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))