add a separate module for fastfoodlayer

main/deepfriedConvnetMnist.py

@@ -12,14 +12,13 @@ Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Son
 
 import tensorflow as tf
 import numpy as np
-import scipy.linalg
-import scipy.stats
 
 import time as t
 
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
 
+from fasfood_layer import fast_food
 
 # --- Usual functions --- #
 
@@ -79,107 +78,6 @@ def random_biases(shape):
     return tf.Variable(b, name="random_biase", trainable=False)
 
 
-# --- Fast Food Naive --- #
-
-def G_variable(shape, trainable=False):
-    """
-    Return a Gaussian Random matrix converted into Tensorflow Variable.
-
-    :param shape: The shape of the matrix (number of fastfood stacks (v), dimension of the input space (d))
-    :type shape: int or tuple of int (tuple size = 2)
-    :return: tf.Variable object containing the matrix, The norm2 of each line (np.array of float)
-    """
-    assert type(shape) == int or (type(shape) == tuple and len(shape) == 2)
-    G = np.random.normal(size=shape).astype(np.float32)
-    G_norms = np.linalg.norm(G, ord=2, axis=1)
-    return tf.Variable(G, name="G", trainable=trainable), G_norms
-
-
-def B_variable(shape, trainable=False):
-    """
-    Return a random matrix of -1 and 1 picked uniformly and converted into Tensorflow Variable.
-
-    :param shape: The shape of the matrix (number of fastfood stacks (v), dimension of the input space (d))
-    :type shape: int or tuple of int (tuple size = 2)
-    :return: tf.Variable object containing the matrix
-    """
-    assert type(shape) == int or (type(shape) == tuple and len(shape) == 2)
-    B = np.random.choice([-1, 1], size=shape, replace=True).astype(np.float32)
-    return tf.Variable(B, name="B", trainable=trainable)
-
-
-def P_variable(d, nbr_stack):
-    """
-    Return a permutation matrix converted into Tensorflow Variable.
-
-    :param d: The width of the matrix (dimension of the input space)
-    :type d: int
-    :param nbr_stack: The height of the matrix (nbr_stack x d is the dimension of the output space)
-    :type nbr_stack: int
-    :return: tf.Variable object containing the matrix
-    """
-    idx = np.hstack([(i * d) + np.random.permutation(d) for i in range(nbr_stack)])
-    P = np.random.permutation(np.eye(N=nbr_stack * d))[idx].astype(np.float32)
-    return tf.Variable(P, name="P", trainable=False)
-
-
-def H_variable(d):
-    """
-    Return an Hadamard matrix converted into Tensorflow Variable.
-
-    d must be a power of two.
-
-    :param d: The size of the Hadamard matrix (dimension of the input space).
-    :type d: int
-    :return: tf.Variable object containing the diagonal and not trainable
-    """
-    H = build_hadamard(d).astype(np.float32)
-    return tf.Variable(H, name="H", trainable=False)
-
-
-def S_variable(shape, G_norms, trainable=False):
-    """
-    Return a scaling matrix of random values picked from a chi distribution.
-
-    The values are re-scaled using the norm of the associated Gaussian random matrix G. The associated Gaussian
-    vectors are the ones generated by the `G_variable` function.
-
-    :param shape: The shape of the matrix (number of fastfood stacks (v), dimension of the input space (d))
-    :type shape: int or tuple of int (tuple size = 2)
-    :param G_norms: The norms of the associated Gaussian random matrices G.
-    :type G_norms: np.array of floats
-    :return: tf.Variable object containing the matrix.
-    """
-    S = np.multiply((1 / G_norms.reshape((-1, 1))), scipy.stats.chi.rvs(shape[1], size=shape).astype(np.float32))
-    return tf.Variable(S, name="S", trainable=trainable)
-
-
-# --- Hadamard utils --- #
-
-def dimensionality_constraints(d):
-    """
-    Enforce d to be a power of 2
-
-    :param d: the original dimension
-    :return: the final dimension
-    """
-    if not is_power_of_two(d):
-        # find d that fulfills 2^l
-        d = np.power(2, np.floor(np.log2(d)) + 1)
-    return d
-
-
-def is_power_of_two(input_integer):
-    """ Test if an integer is a power of two. """
-    if input_integer == 1:
-        return False
-    return input_integer != 0 and ((input_integer & (input_integer - 1)) == 0)
-
-
-def build_hadamard(n_neurons):
-    return scipy.linalg.hadamard(n_neurons)
-
-
 # --- Representation Layer --- #
 
 def random_features(conv_out, sigma):
@@ -195,58 +93,6 @@ def random_features(conv_out, sigma):
         return h1_final
 
 
-def fast_food(conv_out, sigma, nbr_stack=1, trainable=False):
-    """
-    Return a fastfood transform op compatible with tensorflow graph.
-
-    Implementation largely inspired from https://gist.github.com/dougalsutherland/1a3c70e57dd1f64010ab .
-
-    See:
-    "Fastfood | Approximating Kernel Expansions in Loglinear Time" by
-    Quoc Le, Tamas Sarl and Alex Smola.
-
-    :param conv_out: the input of the op
-    :param sigma: bandwith of the gaussian distribution
-    :param nbr_stack: number of fast food stacks
-    :param trainable: the diagonal matrices are trainable or not
-    :return: the output of the fastfood transform
-    """
-    with tf.name_scope("fastfood" + "_sigma-"+str(sigma)):
-        init_dim = np.prod([s.value for s in conv_out.shape if s.value is not None])
-        final_dim = int(dimensionality_constraints(init_dim))
-        padding = final_dim - init_dim
-        conv_out2 = tf.reshape(conv_out, [-1, init_dim])
-        paddings = tf.constant([[0, 0], [0, padding]])
-        conv_out2 = tf.pad(conv_out2, paddings, "CONSTANT")
-
-        G, G_norm = G_variable((nbr_stack, final_dim), trainable=trainable)
-        tf.summary.histogram("weights G", G)
-        B = B_variable((nbr_stack, final_dim), trainable=trainable)
-        tf.summary.histogram("weights B", B)
-        H = H_variable(final_dim)
-        tf.summary.histogram("weights H", H)
-        P = P_variable(final_dim, nbr_stack)
-        tf.summary.histogram("weights P", P)
-        S = S_variable((nbr_stack, final_dim), G_norm, trainable=trainable)
-        tf.summary.histogram("weights S", S)
-
-        conv_out2 = tf.reshape(conv_out2, (1, -1, 1, final_dim))
-        h_ff1 = tf.multiply(conv_out2, B, name="Bx")
-        h_ff1 = tf.reshape(h_ff1, (-1, final_dim))
-        h_ff2 = tf.matmul(h_ff1, H, name="HBx")
-        h_ff2 = tf.reshape(h_ff2, (-1, final_dim * nbr_stack))
-        h_ff3 = tf.matmul(h_ff2, P, name="PHBx")
-        h_ff4 = tf.multiply(tf.reshape(h_ff3, (-1, final_dim * nbr_stack)), tf.reshape(G, (-1, final_dim * nbr_stack)), name="GPHBx")
-        h_ff4 = tf.reshape(h_ff4, (-1, final_dim))
-        h_ff5 = tf.matmul(h_ff4, H, name="HGPHBx")
-
-        h_ff6 = tf.scalar_mul((1/(sigma * np.sqrt(final_dim))), tf.multiply(tf.reshape(h_ff5, (-1, final_dim * nbr_stack)), tf.reshape(S, (-1, final_dim * nbr_stack)), name="SHGPHBx"))
-        h_ff7_1 = tf.cos(h_ff6)
-        h_ff7_2 = tf.sin(h_ff6)
-        h_ff7 = tf.scalar_mul(tf.sqrt(float(1 / final_dim)), tf.concat([h_ff7_1, h_ff7_2], axis=1))
-    return h_ff7
-
-
 def fully_connected(conv_out):
     with tf.name_scope("fc_1"):
         h_pool2_flat = tf.reshape(conv_out, [-1, 7 * 7 * 64])
@@ -264,6 +110,7 @@ def mnist_dims():
     output_dim = int(mnist.train.labels.shape[1])
     return input_dim, output_dim
 
+
 if __name__ == '__main__':
     SIGMA = 5.0
     print("Sigma = {}".format(SIGMA))

main/fasfood_layer.py

+import numpy as np
+import tensorflow as tf
+
+import scipy.linalg
+import scipy.stats
+
+# --- Fast Food Naive --- #
+
+
+def G_variable(shape, trainable=False):
+    """
+    Return a Gaussian Random matrix converted into Tensorflow Variable.
+
+    :param shape: The shape of the matrix (number of fastfood stacks (v), dimension of the input space (d))
+    :type shape: int or tuple of int (tuple size = 2)
+    :return: tf.Variable object containing the matrix, The norm2 of each line (np.array of float)
+    """
+    assert type(shape) == int or (type(shape) == tuple and len(shape) == 2)
+    G = np.random.normal(size=shape).astype(np.float32)
+    G_norms = np.linalg.norm(G, ord=2, axis=1)
+    return tf.Variable(G, name="G", trainable=trainable), G_norms
+
+
+def B_variable(shape, trainable=False):
+    """
+    Return a random matrix of -1 and 1 picked uniformly and converted into Tensorflow Variable.
+
+    :param shape: The shape of the matrix (number of fastfood stacks (v), dimension of the input space (d))
+    :type shape: int or tuple of int (tuple size = 2)
+    :return: tf.Variable object containing the matrix
+    """
+    assert type(shape) == int or (type(shape) == tuple and len(shape) == 2)
+    B = np.random.choice([-1, 1], size=shape, replace=True).astype(np.float32)
+    return tf.Variable(B, name="B", trainable=trainable)
+
+
+def P_variable(d, nbr_stack):
+    """
+    Return a permutation matrix converted into Tensorflow Variable.
+
+    :param d: The width of the matrix (dimension of the input space)
+    :type d: int
+    :param nbr_stack: The height of the matrix (nbr_stack x d is the dimension of the output space)
+    :type nbr_stack: int
+    :return: tf.Variable object containing the matrix
+    """
+    idx = np.hstack([(i * d) + np.random.permutation(d) for i in range(nbr_stack)])
+    P = np.random.permutation(np.eye(N=nbr_stack * d))[idx].astype(np.float32)
+    return tf.Variable(P, name="P", trainable=False)
+
+
+def H_variable(d):
+    """
+    Return an Hadamard matrix converted into Tensorflow Variable.
+
+    d must be a power of two.
+
+    :param d: The size of the Hadamard matrix (dimension of the input space).
+    :type d: int
+    :return: tf.Variable object containing the diagonal and not trainable
+    """
+    H = build_hadamard(d).astype(np.float32)
+    return tf.Variable(H, name="H", trainable=False)
+
+
+def S_variable(shape, G_norms, trainable=False):
+    """
+    Return a scaling matrix of random values picked from a chi distribution.
+
+    The values are re-scaled using the norm of the associated Gaussian random matrix G. The associated Gaussian
+    vectors are the ones generated by the `G_variable` function.
+
+    :param shape: The shape of the matrix (number of fastfood stacks (v), dimension of the input space (d))
+    :type shape: int or tuple of int (tuple size = 2)
+    :param G_norms: The norms of the associated Gaussian random matrices G.
+    :type G_norms: np.array of floats
+    :return: tf.Variable object containing the matrix.
+    """
+    S = np.multiply((1 / G_norms.reshape((-1, 1))), scipy.stats.chi.rvs(shape[1], size=shape).astype(np.float32))
+    return tf.Variable(S, name="S", trainable=trainable)
+
+
+def fast_food(conv_out, sigma, nbr_stack=1, trainable=False):
+    """
+    Return a fastfood transform op compatible with tensorflow graph.
+
+    Implementation largely inspired from https://gist.github.com/dougalsutherland/1a3c70e57dd1f64010ab .
+
+    See:
+    "Fastfood | Approximating Kernel Expansions in Loglinear Time" by
+    Quoc Le, Tamas Sarl and Alex Smola.
+
+    :param conv_out: the input of the op
+    :param sigma: bandwith of the gaussian distribution
+    :param nbr_stack: number of fast food stacks
+    :param trainable: the diagonal matrices are trainable or not
+    :return: the output of the fastfood transform
+    """
+    with tf.name_scope("fastfood" + "_sigma-"+str(sigma)):
+        init_dim = np.prod([s.value for s in conv_out.shape if s.value is not None])
+        final_dim = int(dimensionality_constraints(init_dim))
+        padding = final_dim - init_dim
+        conv_out2 = tf.reshape(conv_out, [-1, init_dim])
+        paddings = tf.constant([[0, 0], [0, padding]])
+        conv_out2 = tf.pad(conv_out2, paddings, "CONSTANT")
+
+        G, G_norm = G_variable((nbr_stack, final_dim), trainable=trainable)
+        tf.summary.histogram("weights G", G)
+        B = B_variable((nbr_stack, final_dim), trainable=trainable)
+        tf.summary.histogram("weights B", B)
+        H = H_variable(final_dim)
+        tf.summary.histogram("weights H", H)
+        P = P_variable(final_dim, nbr_stack)
+        tf.summary.histogram("weights P", P)
+        S = S_variable((nbr_stack, final_dim), G_norm, trainable=trainable)
+        tf.summary.histogram("weights S", S)
+
+        conv_out2 = tf.reshape(conv_out2, (1, -1, 1, final_dim))
+        h_ff1 = tf.multiply(conv_out2, B, name="Bx")
+        h_ff1 = tf.reshape(h_ff1, (-1, final_dim))
+        h_ff2 = tf.matmul(h_ff1, H, name="HBx")
+        h_ff2 = tf.reshape(h_ff2, (-1, final_dim * nbr_stack))
+        h_ff3 = tf.matmul(h_ff2, P, name="PHBx")
+        h_ff4 = tf.multiply(tf.reshape(h_ff3, (-1, final_dim * nbr_stack)), tf.reshape(G, (-1, final_dim * nbr_stack)), name="GPHBx")
+        h_ff4 = tf.reshape(h_ff4, (-1, final_dim))
+        h_ff5 = tf.matmul(h_ff4, H, name="HGPHBx")
+
+        h_ff6 = tf.scalar_mul((1/(sigma * np.sqrt(final_dim))), tf.multiply(tf.reshape(h_ff5, (-1, final_dim * nbr_stack)), tf.reshape(S, (-1, final_dim * nbr_stack)), name="SHGPHBx"))
+        h_ff7_1 = tf.cos(h_ff6)
+        h_ff7_2 = tf.sin(h_ff6)
+        h_ff7 = tf.scalar_mul(tf.sqrt(float(1 / final_dim)), tf.concat([h_ff7_1, h_ff7_2], axis=1))
+    return h_ff7
+
+
+# --- Hadamard utils --- #
+
+def dimensionality_constraints(d):
+    """
+    Enforce d to be a power of 2
+
+    :param d: the original dimension
+    :return: the final dimension
+    """
+    if not is_power_of_two(d):
+        # find d that fulfills 2^l
+        d = np.power(2, np.floor(np.log2(d)) + 1)
+    return d
+
+
+def is_power_of_two(input_integer):
+    """ Test if an integer is a power of two. """
+    if input_integer == 1:
+        return False
+    return input_integer != 0 and ((input_integer & (input_integer - 1)) == 0)
+
+
+def build_hadamard(n_neurons):
+    return scipy.linalg.hadamard(n_neurons)