def mnist_mlp_model(x): paddings = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]], name='pad_const') x = tf.pad(x, paddings) W_conv1 = tf.compat.v1.get_variable('W_conv1', [5, 5, 1, 5]) y = conv2d_stride_2_valid(x, W_conv1) W_bc1 = tf.compat.v1.get_variable('W_conv1_bias', [1, 13, 13, 5]) y = y + W_bc1 y = tf.nn.relu(y) y = max_pool_3x3_same_size(y) W_conv2 = tf.compat.v1.get_variable('W_conv2', [5, 5, 5, 50]) y = conv2d_stride_2_valid(y, W_conv2) y = max_pool_3x3_same_size(y) y = tf.reshape(y, [-1, 5 * 5 * 50]) W_fc1 = tf.compat.v1.get_variable('W_fc1', [5 * 5 * 50, 100]) W_b1 = tf.compat.v1.get_variable('W_fc1_bias', [100]) y = tf.matmul(y, W_fc1) y = y + W_b1 y = tf.nn.relu(y) W_fc2 = tf.compat.v1.get_variable('W_fc2', [100, 10]) W_b2 = tf.compat.v1.get_variable('W_fc2_bias', [10]) y = tf.matmul(y, W_fc2) y = tf.add(y, W_b2, name='output') 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_test_averaged_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_Part1 = np.loadtxt('W_conv1_Part1.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part2 = np.loadtxt('W_conv1_Part2.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part3 = np.loadtxt('W_conv1_Part3.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part4 = np.loadtxt('W_conv1_Part4.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1 = (W_conv1_Part1 + W_conv1_Part2 + W_conv1_Part3 + W_conv1_Part4) *0.25 y = conv2d_stride_2_valid(x, W_conv1) y = tf.square(y) W_squash_Part1 = np.loadtxt('W_squash_Part1.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part2 = np.loadtxt('W_squash_Part2.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part3 = np.loadtxt('W_squash_Part3.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part4 = np.loadtxt('W_squash_Part4.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash = (W_squash_Part1+W_squash_Part2+W_squash_Part3+W_squash_Part4) *0.25 y = tf.reshape(y, [-1, 5 * 13 * 13]) y = tf.matmul(y, W_squash) y = tf.square(y) W_fc2_Part1 = np.loadtxt('W_fc2_Part1.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part2 = np.loadtxt('W_fc2_Part2.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part3 = np.loadtxt('W_fc2_Part3.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part4 = np.loadtxt('W_fc2_Part4.txt',dtype=np.float32).reshape([100, 10]) W_fc2 = (W_fc2_Part1+W_fc2_Part2+W_fc2_Part3+W_fc2_Part4)*0.25 y = tf.matmul(y, W_fc2) return y
def cryptonets_relu_test_squashed(x): """Constructs test network for Cryptonets Relu 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) W_bc1 = get_variable('W_conv1_bias', [1, 13, 13, 5], 'test') y = tf.nn.relu(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) W_b1 = get_variable('W_fc1_bias', [100], 'test') y = y + W_b1 y = tf.nn.relu(y) W_fc2 = get_variable('W_fc2', [100, 10], 'test') y = tf.matmul(y, W_fc2) W_b2 = get_variable('W_fc2_bias', [10], 'test') y = y + W_b2 return y
def cryptonets_HE_avg(x): paddings = [[0, 0], [0, 1], [0, 1], [0, 0]] x = tf.pad(x, paddings) W_conv1 = np.float32(np.load("conv1_avg_dec.npy",allow_pickle=True)) y = conv2d_stride_2_valid(x, W_conv1) y = tf.square(y) W_squash_Part1 = np.loadtxt('W_squash_repeatedSampling_Part1.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part2 = np.loadtxt('W_squash_repeatedSampling_Part2.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part3 = np.loadtxt('W_squash_repeatedSampling_Part3.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part4 = np.loadtxt('W_squash_repeatedSampling_Part4.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash = (W_squash_Part1+W_squash_Part2+W_squash_Part3+W_squash_Part4) *0.25 y = tf.reshape(y, [-1, 5 * 13 * 13]) y = tf.matmul(y, W_squash) y = tf.square(y) W_fc2 = np.float32(np.load("fc2_av_dec.npy",allow_pickle=True)) y = tf.matmul(y, W_fc2) return y
def squash_layers(): print("Squashing layers") tf.compat.v1.reset_default_graph() # Input from h_conv1 squaring x = tf.compat.v1.placeholder(tf.float32, [None, 13, 13, 5]) # Pooling layer h_pool1 = 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 = conv2d_stride_2_valid(h_pool1, W_conv2) # Second pooling layer. h_pool2 = 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.compat.v1.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_test_squashed_mode(x,mode): """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) if mode==1: W_conv1_Part1 = np.loadtxt('W_conv1_InOrderSampling_Part1.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part2 = np.loadtxt('W_conv1_InOrderSampling_Part2.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part3 = np.loadtxt('W_conv1_InOrderSampling_Part3.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part4 = np.loadtxt('W_conv1_InOrderSampling_Part4.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1 = (W_conv1_Part1 + W_conv1_Part2 + W_conv1_Part3 + W_conv1_Part4) *0.25 W_squash_Part1 = np.loadtxt('W_squash_InOrderSampling_Part1.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part2 = np.loadtxt('W_squash_InOrderSampling_Part2.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part3 = np.loadtxt('W_squash_InOrderSampling_Part3.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part4 = np.loadtxt('W_squash_InOrderSampling_Part4.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash = (W_squash_Part1+W_squash_Part2+W_squash_Part3+W_squash_Part4) *0.25 W_fc2_Part1 = np.loadtxt('W_fc2_InOrderSampling_Part1.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part2 = np.loadtxt('W_fc2_InOrderSampling_Part2.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part3 = np.loadtxt('W_fc2_InOrderSampling_Part3.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part4 = np.loadtxt('W_fc2_InOrderSampling_Part4.txt',dtype=np.float32).reshape([100, 10]) W_fc2 = (W_fc2_Part1+W_fc2_Part2+W_fc2_Part3+W_fc2_Part4)*0.25 elif mode==2: W_conv1_Part1 = np.loadtxt('W_conv1_nonRepeatedSampling_Part1.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part2 = np.loadtxt('W_conv1_nonRepeatedSampling_Part2.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part3 = np.loadtxt('W_conv1_nonRepeatedSampling_Part3.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part4 = np.loadtxt('W_conv1_nonRepeatedSampling_Part4.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1 = (W_conv1_Part1 + W_conv1_Part2 + W_conv1_Part3 + W_conv1_Part4) *0.25 W_squash_Part1 = np.loadtxt('W_squash_nonRepeatedSampling_Part1.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part2 = np.loadtxt('W_squash_nonRepeatedSampling_Part2.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part3 = np.loadtxt('W_squash_nonRepeatedSampling_Part3.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part4 = np.loadtxt('W_squash_nonRepeatedSampling_Part4.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash = (W_squash_Part1+W_squash_Part2+W_squash_Part3+W_squash_Part4) *0.25 W_fc2_Part1 = np.loadtxt('W_fc2_nonRepeatedSampling_Part1.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part2 = np.loadtxt('W_fc2_nonRepeatedSampling_Part2.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part3 = np.loadtxt('W_fc2_nonRepeatedSampling_Part3.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part4 = np.loadtxt('W_fc2_nonRepeatedSampling_Part4.txt',dtype=np.float32).reshape([100, 10]) W_fc2 = (W_fc2_Part1+W_fc2_Part2+W_fc2_Part3+W_fc2_Part4)*0.25 else: W_conv1_Part1 = np.loadtxt('W_conv1_repeatedSampling_Part1.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part2 = np.loadtxt('W_conv1_repeatedSampling_Part2.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part3 = np.loadtxt('W_conv1_repeatedSampling_Part3.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1_Part4 = np.loadtxt('W_conv1_repeatedSampling_Part4.txt',dtype=np.float32).reshape([5,5,1,5]) W_conv1 = (W_conv1_Part1 + W_conv1_Part2 + W_conv1_Part3 + W_conv1_Part4) *0.25 W_squash_Part1 = np.loadtxt('W_squash_repeatedSampling_Part1.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part2 = np.loadtxt('W_squash_repeatedSampling_Part2.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part3 = np.loadtxt('W_squash_repeatedSampling_Part3.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash_Part4 = np.loadtxt('W_squash_repeatedSampling_Part4.txt',dtype=np.float32).reshape([5 * 13 * 13, 100]) W_squash = (W_squash_Part1+W_squash_Part2+W_squash_Part3+W_squash_Part4) *0.25 W_fc2_Part1 = np.loadtxt('W_fc2_repeatedSampling_Part1.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part2 = np.loadtxt('W_fc2_repeatedSampling_Part2.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part3 = np.loadtxt('W_fc2_repeatedSampling_Part3.txt',dtype=np.float32).reshape([100, 10]) W_fc2_Part4 = np.loadtxt('W_fc2_repeatedSampling_Part4.txt',dtype=np.float32).reshape([100, 10]) W_fc2 = (W_fc2_Part1+W_fc2_Part2+W_fc2_Part3+W_fc2_Part4)*0.25 y = conv2d_stride_2_valid(x, W_conv1) y = tf.square(y) y = tf.reshape(y, [-1, 5 * 13 * 13]) y = tf.matmul(y, W_squash) y = tf.square(y) y = tf.matmul(y, W_fc2) 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
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'): paddings = [[0, 0], [0, 1], [0, 1], [0, 0]] x = tf.pad(x, paddings) # 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(conv2d_stride_2_valid(x, 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 = 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 = 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 = 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