nystrom approx now use common function for conv relu pooling

main/nystrom/nystrom_approx.py

@@ -5,10 +5,9 @@ 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
+from skluc.neural_networks import bias_variable, weight_variable, conv_relu_pool, get_next_batch
 
 tf.logging.set_verbosity(tf.logging.ERROR)
 
@@ -43,29 +42,12 @@ 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 convolution_mnist(input_, trainable=True):
+    with tf.variable_scope("conv_pool_1"):
+        conv1 = conv_relu_pool(input_, [5, 5, 1, 20], [20], trainable=trainable)
+    with tf.variable_scope("conv_pool_2"):
+        conv2 = conv_relu_pool(conv1, [5, 5, 20, 50], [50], trainable=trainable)
+    return conv2
 
 
 def fully_connected(conv_out):