def cryptonets_test_original(x):
"""Constructs test network for Cryptonets using saved weights"""
# Reshape to use within a convolutional neural net.
# Last dimension is for "features" - there is only one here, since images
# are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
with tf.name_scope('reshape'):
x_image = tf.reshape(x, [-1, 28, 28, 1])
# First conv layer - maps one grayscale image to 5 feature maps of 13 x 13
with tf.name_scope('conv1'):
W_conv1 = tf.constant(
np.loadtxt('W_conv1.txt', dtype=np.float32).reshape([5, 5, 1, 5]))
h_conv1_no_pad = tf.square(
common.conv2d_stride_2_valid(x_image, W_conv1))
paddings = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]],
name='pad_const')
h_conv1 = tf.pad(h_conv1_no_pad, paddings)
# Pooling layer
with tf.name_scope('pool1'):
h_pool1 = common.avg_pool_3x3_same_size(h_conv1) # To 5 x 13 x 13
# Second convolution
with tf.name_scope('conv2'):
W_conv2 = tf.constant(
np.loadtxt('W_conv2.txt', dtype=np.float32).reshape([5, 5, 5, 50]))
h_conv2 = common.conv2d_stride_2_valid(h_pool1, W_conv2)
# Second pooling layer.
with tf.name_scope('pool2'):
h_pool2 = common.avg_pool_3x3_same_size(h_conv2)
# Fully connected layer 1
# Input: N x 5 x 5 x 50
# Output: N x 100
with tf.name_scope('fc1'):
W_fc1 = tf.constant(
np.loadtxt('W_fc1.txt',
dtype=np.float32).reshape([5 * 5 * 50, 100]))
h_pool2_flat = tf.reshape(h_pool2, [-1, 5 * 5 * 50])
h_fc1 = tf.square(tf.matmul(h_pool2_flat, W_fc1))
# Map the 100 features to 10 classes, one for each digit
with tf.name_scope('fc2'):
W_fc2 = tf.constant(
np.loadtxt('W_fc2.txt', dtype=np.float32).reshape([100, 10]))
y_conv = tf.matmul(h_fc1, W_fc2)
return y_conv
def squash_layers():
print("Squashing layers")
tf.reset_default_graph()
# Input from h_conv1 squaring
x = tf.placeholder(tf.float32, [None, 13, 13, 5])
# Pooling layer
h_pool1 = common.avg_pool_3x3_same_size(x) # To N x 13 x 13 x 5
# Second convolution
W_conv2 = np.loadtxt('W_conv2.txt',
dtype=np.float32).reshape([5, 5, 5, 50])
h_conv2 = common.conv2d_stride_2_valid(h_pool1, W_conv2)
# Second pooling layer.
h_pool2 = common.avg_pool_3x3_same_size(h_conv2)
# Fully connected layer 1
# Input: N x 5 x 5 x 50
# Output: N x 100
W_fc1 = np.loadtxt('W_fc1.txt',
dtype=np.float32).reshape([5 * 5 * 50, 100])
h_pool2_flat = tf.reshape(h_pool2, [-1, 5 * 5 * 50])
pre_square = tf.matmul(h_pool2_flat, W_fc1)
with tf.Session() as sess:
x_in = np.eye(13 * 13 * 5)
x_in = x_in.reshape([13 * 13 * 5, 13, 13, 5])
W = (sess.run([pre_square], feed_dict={x: x_in}))[0]
squashed_file_name = "W_squash.txt"
np.savetxt(squashed_file_name, W)
print("Saved to", squashed_file_name)
# Sanity check
x_in = np.random.rand(100, 13, 13, 5)
network_out = (sess.run([pre_square], feed_dict={x: x_in}))[0]
linear_out = x_in.reshape(100, 13 * 13 * 5).dot(W)
assert (np.max(np.abs(linear_out - network_out)) < 1e-5)
print("Squashed layers")
def cryptonets_train(x):
"""Builds the graph for classifying digits based on Cryptonets
Args:
x: an input tensor with the dimensions (N_examples, 784), where 784 is
the number of pixels in a standard MNIST image.
Returns:
A tuple (y, a scalar placeholder). y is a tensor of shape
(N_examples, 10), with values equal to the logits of classifying the
digit into one of 10 classes (the digits 0-9).
"""
# Reshape to use within a conv neural net.
# Last dimension is for "features" - there is only one here, since images
# are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
with tf.name_scope('reshape'):
x_image = tf.reshape(x, [-1, 28, 28, 1])
# First conv layer
# CryptoNets's output of the first conv layer has feature map size 13 x 13,
# therefore, we manually add paddings.
# Input: N x 28 x 28 x 1
# Filter: 5 x 5 x 1 x 5
# Output: N x 12 x 12 x 5
# Output after padding: N x 13 x 13 x 5
with tf.name_scope('conv1'):
W_conv1 = tf.get_variable("W_conv1", [5, 5, 1, 5])
h_conv1_no_pad = tf.square(
common.conv2d_stride_2_valid(x_image, W_conv1))
paddings = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]],
name='pad_const')
h_conv1 = tf.pad(h_conv1_no_pad, paddings)
# Pooling layer
# Input: N x 13 x 13 x 5
# Output: N x 13 x 13 x 5
with tf.name_scope('pool1'):
h_pool1 = common.avg_pool_3x3_same_size(h_conv1)
# Second convolution
# Input: N x 13 x 13 x 5
# Filter: 5 x 5 x 5 x 50
# Output: N x 5 x 5 x 50
with tf.name_scope('conv2'):
W_conv2 = tf.get_variable("W_conv2", [5, 5, 5, 50])
h_conv2 = common.conv2d_stride_2_valid(h_pool1, W_conv2)
# Second pooling layer
# Input: N x 5 x 5 x 50
# Output: N x 5 x 5 x 50
with tf.name_scope('pool2'):
h_pool2 = common.avg_pool_3x3_same_size(h_conv2)
# Fully connected layer 1
# Input: N x 5 x 5 x 50
# Input flattened: N x 1250
# Weight: 1250 x 100
# Output: N x 100
with tf.name_scope('fc1'):
h_pool2_flat = tf.reshape(h_pool2, [-1, 5 * 5 * 50])
W_fc1 = tf.get_variable("W_fc1", [5 * 5 * 50, 100])
h_fc1 = tf.square(tf.matmul(h_pool2_flat, W_fc1))
# Map the 100 features to 10 classes, one for each digit
# Input: N x 100
# Weight: 100 x 10
# Output: N x 10
with tf.name_scope('fc2'):
W_fc2 = tf.get_variable("W_fc2", [100, 10])
y_conv = tf.matmul(h_fc1, W_fc2)
return y_conv