diff --git a/.gitignore b/.gitignore
index 25edce6a975cd781255e0291d7d4199b671a1b64..1df246617e40f1c0e38a0d80f3483a7ae009a721 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
.idea
+MNIST_data/
# Byte-compiled / optimized / DLL files
__pycache__/
diff --git a/main/convnet_random.py b/main/convnet_random.py
index b6b11c8256fa7d42cd7ae1351576215ae7c29c1b..2b1bc1332bf289997b64040155d686a4e2ea1dda 100644
--- a/main/convnet_random.py
+++ b/main/convnet_random.py
@@ -4,6 +4,10 @@ Convolutional Neural Netwok implementation in tensorflow whith multiple represen
- Random Fourier Features layer
- Fast Food layer where Fast Hadamard Transform has been replaced by dot product with Hadamard matrix.
+See:
+"Deep Fried Convnets" by
+Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Song, Ziyu Wang
+
"""
import tensorflow as tf
@@ -77,59 +81,55 @@ def random_biases(shape):
# --- Fast Food Naive --- #
-def G_variable(d, diag=True, trainable=False):
+def G_variable(shape, trainable=False):
"""
- Return a Gaussian Random diagonal matrix converted into Tensorflow Variable.
+ Return a Gaussian Random matrix converted into Tensorflow Variable.
- :param d: The size of the diagonal
- :type d: int
- :return: tf.Variable object containing the diagonal and not trainable, The frobenius norm of this diagonal (float)
+ :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
- if diag:
- G = np.diag(np.random.normal(size=d)).astype(np.float32)
- G_norm = np.linalg.norm(G, ord='fro')
- else:
- G = np.random.normal(size=d).astype(np.float32)
- G_norm = np.linalg.norm(G, ord=2)
- return tf.Variable(G, name="G", trainable=trainable), G_norm
-
-def B_variable(d, diag=True, trainable=False):
+def B_variable(shape, trainable=False):
"""
- Return a random diagonal matrix of -1 and 1 picked uniformly into Tensorflow Variable.
+ Return a random matrix of -1 and 1 picked uniformly and converted into Tensorflow Variable.
- :param d: The size of the diagonal
- :type d: int
- :return: tf.Variable object containing the diagonal and not trainable
+ :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
"""
- if diag:
- B = np.diag(np.random.choice([-1, 1], size=d, replace=True)).astype(np.float32)
- else:
- B = np.random.choice([-1, 1], size=d, replace=True).astype(np.float32)
+ 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):
+def P_variable(d, nbr_stack):
"""
- Return a permutation matrix into Tensorflow Variable.
+ Return a permutation matrix converted into Tensorflow Variable.
- :param d: The size of the diagonal
+ :param d: The width of the matrix (dimension of the input space)
:type d: int
- :return: tf.Variable object containing the diagonal and not trainable
+ :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.random.permutation(d)
- P = np.random.permutation(np.eye(N=d))[idx].astype(np.float32)
+ idx = [(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 into Tensorflow Variable.
+ Return an Hadamard matrix converted into Tensorflow Variable.
d must be a power of two.
- :param d: The size of the Hadamard matrix.
+ :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
"""
@@ -137,22 +137,20 @@ def H_variable(d):
return tf.Variable(H, name="H", trainable=False)
-def S_variable(d, G_norm, diag=True, trainable=False):
+def S_variable(shape, G_norms, trainable=False):
"""
- Return a scaling diagonal matrix of random values picked from a chi distribution.
+ Return a scaling matrix of random values picked from a chi distribution.
- The values are re-scaled using the norm of the Gaussian Diagonal random matrix G.
+ 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 d: The size of the diagonal.
- :type d: int
- :param G_norm: The norm of the Gaussian Diagonal random matrix G.
- :type G_norm: float
- :return: tf.Variable object containing the diagonal and not trainable.
+ :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.
"""
- if diag:
- S = np.diag((1 / G_norm) * scipy.stats.chi.rvs(d, size=d)).astype(np.float32)
- else:
- S = (1 / G_norm) * scipy.stats.chi.rvs(d, size=d).astype(np.float32)
+ 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)
@@ -197,8 +195,23 @@ def random_features(conv_out, sigma):
return h1_final
-def fast_food(conv_out, sigma, nbr_stack=1, diag=True, trainable=False, name="fastfood"):
- with tf.name_scope(name + "_diag=" + str(diag) + "_sigma=" + str(sigma)):
+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
@@ -206,40 +219,31 @@ def fast_food(conv_out, sigma, nbr_stack=1, diag=True, trainable=False, name="fa
paddings = tf.constant([[0, 0], [0, padding]])
conv_out2 = tf.pad(conv_out2, paddings, "CONSTANT")
- G, G_norm = G_variable(final_dim, diag=diag, trainable=trainable)
+ G, G_norm = G_variable((nbr_stack, final_dim), trainable=trainable)
tf.summary.histogram("weights G", G)
- B = B_variable(final_dim, diag=diag, trainable=trainable)
+ 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)
+ P = P_variable(final_dim, nbr_stack)
tf.summary.histogram("weights P", P)
- S = S_variable(final_dim, G_norm, diag=diag, trainable=trainable)
+ S = S_variable((nbr_stack, final_dim), G_norm, trainable=trainable)
tf.summary.histogram("weights S", S)
- if diag:
- h_ff1 = tf.matmul(conv_out2, B, name="Bx")
- h_ff2 = tf.matmul(h_ff1, H, name="HBx")
- h_ff3 = tf.matmul(h_ff2, P, name="PHBx")
- h_ff4 = tf.matmul(h_ff3, G, name="GPHBx")
- h_ff5 = tf.matmul(h_ff4, H, name="HGPHBx")
-
- h_ff6 = tf.scalar_mul((1/(sigma * np.sqrt(final_dim))), tf.matmul(h_ff5, S, 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))
-
- else:
- h_ff1 = tf.multiply(conv_out2, B, name="Bx")
- h_ff2 = tf.matmul(h_ff1, H, name="HBx")
- h_ff3 = tf.matmul(h_ff2, P, name="PHBx")
- h_ff4 = tf.multiply(h_ff3, G, name="GPHBx")
- h_ff5 = tf.matmul(h_ff4, H, name="HGPHBx")
-
- h_ff6 = tf.scalar_mul((1/(sigma * np.sqrt(final_dim))), tf.multiply(h_ff5, S, 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))
+ 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
@@ -255,15 +259,6 @@ def fully_connected(conv_out):
return h_fc1
-def stacked_fastfood(input_, nbr, sigma, diag=False, trainable=False):
- l_outputs = []
- for i in range(nbr):
- output = fast_food(input_, sigma, diag=diag, trainable=trainable, name="fastfood" + str(i))
- l_outputs.append(output)
- outputs_stacked = tf.concat(l_outputs, axis=1)
- return outputs_stacked
-
-
if __name__ == '__main__':
SIGMA = 5.0
print("Sigma = {}".format(SIGMA))
@@ -281,12 +276,10 @@ if __name__ == '__main__':
h_conv = convolution(x_image)
# h_conv = x
# out_fc = fully_connected(h_conv) # 95% accuracy
- # out_fc = tf.nn.relu(fast_food(h_conv, SIGMA)) # 83% accuracy (conv) | 56% accuracy (noconv)
- # out_fc = tf.nn.relu(fast_food(h_conv, SIGMA, diag=False)) # 84% accuracy (conv) | 59% accuracy (noconv)
- # out_fc = tf.nn.relu(fast_food(h_conv, SIGMA, diag=False, trainable=True)) # 84% accuracy (conv) | 59% accuracy (noconv)
- # todo: faire une implémentation moins naive: il doit y avoir des blocs dans tf uniquement lorsque j'utilise des matrices
- # diagonales, sinon je n'ai besoin que de plusieurs lignes pour la matrice de hadamard
- out_fc = tf.nn.relu(stacked_fastfood(h_conv, 2, SIGMA, diag=False, trainable=True)) # 84% accuracy (conv) | 59% accuracy (noconv)
+ # out_fc = tf.nn.relu(fast_food(h_conv, SIGMA, nbr_stack=1)) # 83% accuracy (conv) | 56% accuracy (noconv)
+ out_fc = tf.nn.relu(fast_food(h_conv, SIGMA, nbr_stack=2))
+ # out_fc = tf.nn.relu(fast_food(h_conv, SIGMA, nbr_stack=2, trainable=True))
+ # out_fc = tf.nn.relu(fast_food(h_conv, SIGMA, trainable=True)) # 84% accuracy (conv) | 59% accuracy (noconv)
# out_fc = fast_food(h_conv, SIGMA, diag=True, trainable=True) # 84% accuracy (conv) | 59% accuracy (noconv)
# out_fc = random_features(h_conv, SIGMA) # 82% accuracy (conv) | 47% accuracy (noconv)
@@ -333,7 +326,7 @@ if __name__ == '__main__':
sess.run(init)
# actual learning
started = t.time()
- for i in range(2000):
+ for i in range(20000):
batch = mnist.train.next_batch(64)
feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5}
# le _ est pour capturer le retour de "train_optimizer" qu'il faut appeler