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"""
Convnet with nystrom approximation of the feature map.
"""
import tensorflow as tf
import numpy as np
from sklearn.metrics.pairwise import rbf_kernel
import skluc.mldatasets as dataset
from skluc.neural_networks import bias_variable, weight_variable, conv2d, max_pool_2x2, conv_relu_pool, 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]]
Y_val = Y_train[permut[:val_size]]
X_train = X_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)
NYSTROM_SAMPLE_SIZE = 500
X_nystrom = X_train[np.random.permutation(NYSTROM_SAMPLE_SIZE)]
def convolution_mnist(input, trainable=True):
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], trainable=trainable)
tf.summary.histogram("weights conv1", W_conv1)
b_conv1 = bias_variable([20], trainable=trainable)
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], trainable=trainable)
tf.summary.histogram("weights conv2", W_conv2)
b_conv2 = bias_variable([50], trainable=trainable)
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
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
def tf_rbf_kernel(X, Y, gamma):
r1 = tf.reduce_sum(X * X, axis=1)
r1 = tf.reshape(r1, [-1, 1])
r2 = tf.reduce_sum(Y * Y, axis=1)
r2 = tf.reshape(r2, [1, -1])
K = tf.matmul(X, tf.transpose(Y))
K = r1 - 2 * K + r2
K *= -gamma
K = tf.exp(K)
return K
def nystrom_layer(input_x, input_subsample, gamma):
with tf.name_scope("nystrom"):
init_dim = np.prod([s.value for s in input_x.shape[1:] if s.value is not None])
h_conv_flat = tf.reshape(input_x, [-1, init_dim])
h_conv_nystrom_subsample_flat = tf.reshape(input_subsample, [NYSTROM_SAMPLE_SIZE, init_dim])
with tf.name_scope("kernel_vec"):
kernel_vector = tf_rbf_kernel(h_conv_flat, h_conv_nystrom_subsample_flat, gamma=gamma)
D = weight_variable((NYSTROM_SAMPLE_SIZE,))
V = weight_variable((NYSTROM_SAMPLE_SIZE, NYSTROM_SAMPLE_SIZE))
out_fc = tf.matmul(kernel_vector, tf.matmul(tf.multiply(D, V), tf.transpose(V)))
return out_fc
def main():
GAMMA = 0.001
print("Gamma = {}".format(GAMMA))
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")
x_nystrom = tf.Variable(X_nystrom, name="nystrom_subsample", trainable=False)
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])
x_nystrom_image = tf.reshape(x_nystrom, [NYSTROM_SAMPLE_SIZE, side_size, side_size, 1])
tf.summary.image("digit", x_image, max_outputs=3)
# Representation layer
with tf.variable_scope("convolution_mnist") as scope_conv_mnist:
h_conv = convolution_mnist(x_image)
scope_conv_mnist.reuse_variables()
h_conv_nystrom_subsample = convolution_mnist(x_nystrom_image, trainable=False)
out_fc = nystrom_layer(h_conv, h_conv_nystrom_subsample, GAMMA)
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# 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(10000):
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))
if __name__ == '__main__':