"""
Convolutional Neural Netwok implementation in tensorflow whith multiple representations possible after the convolution:
- Fully connected layer
- 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- Fast Food layer where Fast Hadamard Transform has been replaced by dot product with Hadamard matrix.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
import numpy as np
import skluc.mldatasets as dataset
from skluc.neural_networks import bias_variable, weight_variable, conv2d, max_pool_2x2, get_next_batch
tf.logging.set_verbosity(tf.logging.ERROR)
import time as t
from sklearn.preprocessing import LabelBinarizer
enc = LabelBinarizer()
mnist = dataset.MnistDataset()
mnist = mnist.load()
X_train, Y_train = mnist["train"]
X_train = np.array(X_train / 255)
enc.fit(Y_train)
Y_train = np.array(enc.transform(Y_train))
X_test, Y_test = mnist["test"]
X_test = np.array(X_test / 255)
Y_test = np.array(enc.transform(Y_test))
X_train = X_train.astype(np.float32)
permut = np.random.permutation(X_train.shape[0])
val_size = 5000
X_val = X_train[permut[:val_size]]
X_train = X_train[permut[val_size:]]
Y_val = Y_train[permut[:val_size]]
Y_train = Y_train[permut[val_size:]]
X_test = X_test.astype(np.float32)
Y_train = Y_train.astype(np.float32)
Y_test = Y_test.astype(np.float32)
from fasfood_layer import fast_food
# todo utiliser les fonctions adapate/definies pour ces couches de convolution
def convolution_mnist(input):
with tf.name_scope("conv_pool_1"):
# 32 is the number of filter we'll use. e.g. the number of different
# shapes this layer is able to recognize
W_conv1 = weight_variable([5, 5, 1, 20])
tf.summary.histogram("weights conv1", W_conv1)
b_conv1 = bias_variable([20])
tf.summary.histogram("biases conv1", b_conv1)
# -1 is here to keep the total size constant (784)
h_conv1 = tf.nn.relu(conv2d(input, W_conv1) + b_conv1)
tf.summary.histogram("act conv1", h_conv1)
h_pool1 = max_pool_2x2(h_conv1)
with tf.name_scope("conv_pool_2"):
W_conv2 = weight_variable([5, 5, 20, 50])
tf.summary.histogram("weights conv2", W_conv2)
b_conv2 = bias_variable([50])
tf.summary.histogram("biases conv2", b_conv2)
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
tf.summary.histogram("act conv2", h_conv2)
h_pool2 = max_pool_2x2(h_conv2)
return h_pool2
# --- Random Fourier Features --- #
def random_variable(shape, sigma):
W = np.random.normal(size=shape, scale=sigma).astype(np.float32)
return tf.Variable(W, name="random_Weights", trainable=False)
def random_biases(shape):
b = np.random.uniform(0, 2 * np.pi, size=shape).astype(np.float32)
return tf.Variable(b, name="random_biase", trainable=False)
# --- Representation Layer --- #
def random_features(conv_out, sigma):
with tf.name_scope("random_features"):
init_dim = np.prod([s.value for s in conv_out.shape if s.value is not None])
conv_out2 = tf.reshape(conv_out, [-1, init_dim])
W = random_variable((init_dim, init_dim), sigma)
b = random_biases(init_dim)
h1 = tf.matmul(conv_out2, W, name="Wx") + b
h1_cos = tf.cos(h1)
h1_final = tf.scalar_mul(np.sqrt(2.0 / init_dim).astype(np.float32), h1_cos)
return h1_final
def fully_connected(conv_out):
with tf.name_scope("fc_1"):
init_dim = np.prod([s.value for s in conv_out.shape if s.value is not None])
h_pool2_flat = tf.reshape(conv_out, [-1, init_dim])
W_fc1 = weight_variable([init_dim, 4096*2])
b_fc1 = bias_variable([4096*2])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
tf.summary.histogram("weights", W_fc1)
tf.summary.histogram("biases", b_fc1)
return h_fc1
if __name__ == '__main__':
SIGMA = 5.0
print("Sigma = {}".format(SIGMA))
with tf.Graph().as_default():
input_dim, output_dim = X_train.shape[1], Y_train.shape[1]
x = tf.placeholder(tf.float32, shape=[None, input_dim], name="x")
y_ = tf.placeholder(tf.float32, shape=[None, output_dim], name="labels")
# side size is width or height of the images
side_size = int(np.sqrt(input_dim))
x_image = tf.reshape(x, [-1, side_size, side_size, 1])
tf.summary.image("digit", x_image, max_outputs=3)
# Representation layer
h_conv = convolution_mnist(x_image)
# h_conv = x
# out_fc = fully_connected(h_conv) # 95% accuracy
# 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)
# classification
with tf.name_scope("fc_2"):
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
h_fc1_drop = tf.nn.dropout(out_fc, keep_prob)
dim = np.prod([s.value for s in h_fc1_drop.shape if s.value is not None])
W_fc2 = weight_variable([dim, output_dim])
b_fc2 = bias_variable([output_dim])
tf.summary.histogram("weights", W_fc2)
tf.summary.histogram("biases", b_fc2)
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# calcul de la loss
with tf.name_scope("xent"):
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv, name="xentropy"),
name="xentropy_mean")
tf.summary.scalar('loss-xent', cross_entropy)
# calcul du gradient
with tf.name_scope("train"):
global_step = tf.Variable(0, name="global_step", trainable=False)
train_optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cross_entropy, global_step=global_step)
# calcul de l'accuracy
with tf.name_scope("accuracy"):
predictions = tf.argmax(y_conv, 1)
correct_prediction = tf.equal(predictions, tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
merged_summary = tf.summary.merge_all()
init = tf.global_variables_initializer()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter("results_deepfried_stacked")
summary_writer.add_graph(sess.graph)
# Initialize all Variable objects
sess.run(init)
# actual learning
started = t.time()
feed_dict_val = {x: X_val, y_: Y_val, keep_prob: 1.0}
for i in range(1100):
X_batch = get_next_batch(X_train, i, 64)
Y_batch = get_next_batch(Y_train, i, 64)
feed_dict = {x: X_batch, y_: Y_batch, keep_prob: 0.5}
# le _ est pour capturer le retour de "train_optimizer" qu'il faut appeler
# pour calculer le gradient mais dont l'output ne nous interesse pas
_, loss, y_result, x_exp = sess.run([train_optimizer, cross_entropy, y_conv, x_image], feed_dict=feed_dict)
if i % 100 == 0:
print('step {}, loss {} (with dropout)'.format(i, loss))
r_accuracy = sess.run([accuracy], feed_dict=feed_dict_val)
print("accuracy: {} on validation set (without dropout).".format(r_accuracy))
summary_str = sess.run(merged_summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, i)
stoped = t.time()
accuracy, preds = sess.run([accuracy, predictions], feed_dict={
x: X_test, y_: Y_test, keep_prob: 1.0})
print('test accuracy %g' % accuracy)
np.set_printoptions(threshold=np.nan)
print("Prediction sample: " + str(preds[:50]))
print("Actual values: " + str(np.argmax(Y_test[:50], axis=1)))
print("Elapsed time: %.4f s" % (stoped - started))