def cryptonets_test_squashed(x): """Constructs test network for Cryptonets using saved weights. Assumes linear layers have been squashed.""" paddings = [[0, 0], [0, 1], [0, 1], [0, 0]] x = tf.pad(x, paddings) W_conv1 = get_variable('W_conv1', [5, 5, 1, 5], 'test') y = conv2d_stride_2_valid(x, W_conv1) y = tf.square(y) W_squash = get_variable('W_squash', [5 * 13 * 13, 100], 'test') y = tf.reshape(y, [-1, 5 * 13 * 13]) y = tf.matmul(y, W_squash) y = tf.square(y) W_fc2 = get_variable('W_fc2', [100, 10], 'test') y = tf.matmul(y, W_fc2) return y
def cryptonets_relu_model(x, mode): if mode not in set(['train', 'test']): print('mode should be train or test') raise Exception() paddings = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]], name='pad_const') x = tf.pad(x, paddings) W_conv1 = get_variable('W_conv1', [5, 5, 1, 5], mode) y = conv2d_stride_2_valid(x, W_conv1) W_bc1 = get_variable('W_conv1_bias', [1, 13, 13, 5], mode) y = y + W_bc1 y = tf.nn.relu(y) y = avg_pool_3x3_same_size(y) W_conv2 = get_variable('W_conv2', [5, 5, 5, 50], mode) y = conv2d_stride_2_valid(y, W_conv2) y = avg_pool_3x3_same_size(y) y = tf.reshape(y, [-1, 5 * 5 * 50]) W_fc1 = get_variable('W_fc1', [5 * 5 * 50, 100], mode) W_b1 = get_variable('W_fc1_bias', [100], mode) y = tf.matmul(y, W_fc1) y = y + W_b1 y = tf.nn.relu(y) W_fc2 = get_variable('W_fc2', [100, 10], mode) W_b2 = get_variable('W_fc2_bias', [10], mode) y = tf.matmul(y, W_fc2) y = y + W_b2 return y
def cryptonets_model(x, mode): """Builds the graph for classifying digits based on Cryptonets Args: x: an input tensor with the dimensions (N_examples, 28, 28) 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). """ if mode not in set(['train', 'test']): print('mode should be train or test') raise Exception() # 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. # CryptoNets's output of the first conv layer has feature map size 13 x 13, # therefore, we manually add paddings. with tf.name_scope('reshape'): print('padding') paddings = [[0, 0], [0, 1], [0, 1], [0, 0]] x = tf.pad(x, paddings) print('padded') # First conv layer # Input: N x 28 x 28 x 1 # Filter: 5 x 5 x 1 x 5 # Output: N x 13 x 13 x 5 with tf.name_scope('conv1'): W_conv1 = get_variable("W_conv1", [5, 5, 1, 5], mode) h_conv1 = tf.square(conv2d_stride_2_valid(x, W_conv1)) # 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 = 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 = get_variable("W_conv2", [5, 5, 5, 50], mode) h_conv2 = 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 = 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 = get_variable("W_fc1", [5 * 5 * 50, 100], mode) 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 = get_variable("W_fc2", [100, 10], mode) y_conv = tf.matmul(h_fc1, W_fc2) return y_conv