first try for nystrom layer

main/nystrom/nystrom_approx.py

+"""
+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 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)
+
+        init_dim = np.prod([s.value for s in h_conv.shape[1:] if s.value is not None])
+        h_conv_flat = tf.reshape(h_conv, [-1, init_dim])
+        h_conv_nystrom_subsample_flat = tf.reshape(h_conv_nystrom_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)))
+
+        # 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, k_vec, eigenvec = sess.run([train_optimizer, cross_entropy, y_conv, x_image, kernel_vector, V], feed_dict=feed_dict)
+            if i % 100 == 0:
+                print(k_vec[0])
+                print("Difference with identity:", np.linalg.norm(eigenvec - np.eye(*eigenvec.shape)))
+                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__':
+    main()
\ No newline at end of file

main/test/test_nystromtf.py

+import unittest
+import tensorflow as tf
+import numpy as np
+from sklearn.metrics.pairwise import rbf_kernel
+
+import skluc.mldatasets as dataset
+
+from main.nystrom.nystrom_approx import tf_rbf_kernel
+
+
+class TestNystrom(unittest.TestCase):
+    def setUp(self):
+        mnist = dataset.MnistDataset()
+        mnist = mnist.load()
+        X_train, Y_train = mnist["train"]
+        X_train = np.array(X_train / 255)
+        X_test, Y_test = mnist["test"]
+        X_test = np.array(X_test / 255)
+        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)
+
+        self.X_val = X_val
+        self.Y_val = Y_val
+        self.X_train = X_train
+        self.Y_train = Y_train
+        self.X_test = X_test
+        self.Y_test = Y_test
+
+        # todo retirer ça
+        self.X_val = self.X_val[:100]
+
+        self.sess = tf.InteractiveSession()
+
+    def test_tf_rbf_kernel(self):
+        gamma = 0.01
+        expected_rbf_kernel = rbf_kernel(self.X_val, self.X_val, gamma=gamma)
+        obtained_rbf_kernel = tf_rbf_kernel(self.X_val, self.X_val, gamma=gamma).eval()
+        difference_rbf_kernel = np.linalg.norm(expected_rbf_kernel - obtained_rbf_kernel)
+        self.assertAlmostEqual(difference_rbf_kernel, 0, delta=1e-5)
+
+        example1 = self.X_val[0].reshape((1, -1))
+        example2 = self.X_val[1].reshape((1, -1))
+        expected_rbf_kernel_value = rbf_kernel(example1, example2, gamma=gamma)
+        obtained_rbf_kernel_value = tf_rbf_kernel(example1, example2, gamma=gamma).eval()
+        difference_rbf_kernel_value = np.linalg.norm(expected_rbf_kernel_value - obtained_rbf_kernel_value)
+        self.assertAlmostEqual(difference_rbf_kernel_value, 0, delta=1e-5)
+
+        expected_rbf_kernel_vector = rbf_kernel(example1, self.X_val, gamma=gamma)
+        obtained_rbf_kernel_vector = tf_rbf_kernel(example1, self.X_val, gamma=gamma).eval()
+        difference_rbf_kernel_vector = np.linalg.norm(expected_rbf_kernel_vector - obtained_rbf_kernel_vector)
+        self.assertAlmostEqual(difference_rbf_kernel_vector, 0, delta=1e-5)
+
+    def tearDown(self):
+        self.sess.close()
+
+
+if __name__ == '__main__':
+    unittest.main()