diff --git a/main/timeit.py b/main/timeit.py
index 383368066351cde295c312750f037b793bc898c0..f06a7720ae3300162f63bee76b9785eacd3f121a 100644
--- a/main/timeit.py
+++ b/main/timeit.py
@@ -3,18 +3,17 @@ import numpy as np
from sklearn.metrics.pairwise import rbf_kernel
import skluc.mldatasets as dataset
-from skluc.neural_networks import fully_connected, get_next_batch, tf_op
+from skluc.neural_networks import fully_connected, get_next_batch, tf_op, conv_relu_pool
from skluc.utils import time_fct
-from nystrom.nystrom_approx import tf_rbf_kernel, convolution_mnist
-import sklearn as sk
+from skluc.nystrom.nystrom_approx import tf_rbf_kernel
tf.logging.set_verbosity(tf.logging.ERROR)
# Preparing the dataset #########################
mnist = dataset.MnistDataset()
-mnist = mnist.load()
-X_train, _ = mnist["train"]
+mnist.load()
+X_train, _ = mnist.train
X_train = np.array(X_train / 255)
X_train = X_train.astype(np.float32)
@@ -24,8 +23,8 @@ X_train = X_train.astype(np.float32)
if __name__ == '__main__':
input_dim = X_train.shape[1]
output_dim_fc = 4096*2
- batch_size = 10
- subsample_size = 100
+ batch_size = 500
+ subsample_size = 500
X_batch = get_next_batch(X_train, 0, batch_size)
X_subsample = get_next_batch(X_train, 0, subsample_size)
@@ -40,12 +39,13 @@ if __name__ == '__main__':
x_subsample_image = tf.reshape(x_subsample, [subsample_size, side_size, side_size, 1])
# fully connected ops
- out_fc_x = fully_connected(x, output_dim_fc, act=tf.nn.relu)
- out_fc_subsample = fully_connected(x_subsample, output_dim_fc, act=tf.nn.relu)
+ out_fc_x = fully_connected(x, output_dim_fc, act=tf.nn.relu, variable_scope="fc_x")
+ out_fc_subsample = fully_connected(x_subsample, output_dim_fc, act=tf.nn.relu, variable_scope="fc_subsample")
# convolution ops
- out_conv_x = convolution_mnist(x_image)
- out_conv_subsample = convolution_mnist(x_subsample_image)
+ out_conv_x = conv_relu_pool(x_image, [5, 5, 1, 20], [20], pool_size=3, variable_scope="conv_x")
+ out_conv_subsample = conv_relu_pool(x_subsample_image, [5, 5, 1, 20], [20], pool_size=3,
+ variable_scope="conv_subsample")
init_dim = np.prod([s.value for s in out_conv_x.shape[1:] if s.value is not None])
x_conv_flat = tf.reshape(out_conv_x, [-1, init_dim])
@@ -66,23 +66,21 @@ if __name__ == '__main__':
x, y = sess.run([x_conv_flat, subsample_conv_flat], feed_dict=feed_dict)
rbf_kernel(x, y, gamma=0.001)
-
- # todo regarder le temps de la retro propagation
- # todo kernel tensorflow on cpu
- # todo kernel tensorflow on gpu
- # todo kernel sklearn on cpu
-
d_time_results = {
- "fc_x": time_fct(lambda: tf_op(feed_dict, [out_fc_x])),
- "fc_subsample": time_fct(lambda: tf_op(feed_dict, [out_fc_subsample])),
- "reshape_x": time_fct(lambda: tf_op(feed_dict, [x_image])),
- "reshape_subsample": time_fct(lambda: tf_op(feed_dict, [x_subsample_image])),
- "reshape_x + conv_x": time_fct(lambda: tf_op(feed_dict, [out_conv_x])),
- "reshape_subsample + conv_subsample": time_fct(lambda: tf_op(feed_dict, [out_fc_subsample])),
- "reshape_x + conv_x + reshape_subsample + conv_subsample + kernel_cpu": time_fct(lambda: tf_op(feed_dict, [kernel_cpu])),
- "reshape_x + conv_x + reshape_subsample + conv_subsample + kernel_gpu": time_fct(lambda: tf_op(feed_dict, [kernel_gpu])),
- "reshape_x + conv_x + reshape_subsample + conv_subsample + kernel_sklearn": time_fct(kernel_sklearn)
+ "fc_x": lambda: time_fct(lambda: tf_op(feed_dict, [out_fc_x]), n_iter=10),
+ "fc_subsample": lambda: time_fct(lambda: tf_op(feed_dict, [out_fc_subsample]), n_iter=10),
+ "reshape_x": lambda: time_fct(lambda: tf_op(feed_dict, [x_image]), n_iter=10),
+ "reshape_subsample": lambda: time_fct(lambda: tf_op(feed_dict, [x_subsample_image]), n_iter=10),
+ "reshape_x + conv_x": lambda: time_fct(lambda: tf_op(feed_dict, [out_conv_x]), n_iter=10),
+ "reshape_subsample + conv_subsample": lambda: time_fct(lambda: tf_op(feed_dict, [out_conv_subsample]), n_iter=10),
+ "reshape_x + conv_x + reshape_subsample + conv_subsample": lambda: time_fct(lambda: tf_op(feed_dict, [out_conv_x, out_conv_subsample]), n_iter=10),
+ "reshape_x + conv_x + reshape_subsample + conv_subsample + kernel_cpu": lambda: time_fct(lambda: tf_op(feed_dict, [kernel_cpu]), n_iter=10),
+ "reshape_x + conv_x + reshape_subsample + conv_subsample + kernel_gpu": lambda: time_fct(lambda: tf_op(feed_dict, [kernel_gpu]), n_iter=10),
+ "reshape_x + conv_x + reshape_subsample + conv_subsample + kernel_sklearn": lambda: time_fct(kernel_sklearn, n_iter=10)
}
for key, value in d_time_results.items():
- print("{}:\t{:.4f}".format(key, value))
+ print("{}:\t{:.4f}s".format(key, value()))
+ tf.reset_default_graph()
+
+ # todo renome ce fichier