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hw3_convolutional_networks/Gabriel/05_convolutional_net.py

+#!/usr/bin/env python
+
+import tensorflow as tf
+import numpy as np
+from tensorflow.examples.tutorials.mnist import input_data
+
+batch_size = 128
+test_size = 5000
+
+def init_weights(shape):
+    return tf.Variable(tf.random_normal(shape, stddev=0.01))
+
+
+def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
+    l1a = tf.nn.relu(tf.nn.conv2d(X, w,                       # l1a shape=(?, 28, 28, 32)
+                        strides=[1, 1, 1, 1], padding='SAME'))
+    l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1],              # l1 shape=(?, 14, 14, 32)
+                        strides=[1, 2, 2, 1], padding='SAME')
+    l1 = tf.nn.dropout(l1, p_keep_conv)
+
+    l2a = tf.nn.relu(tf.nn.conv2d(l1, w2,                     # l2a shape=(?, 14, 14, 64)
+                        strides=[1, 1, 1, 1], padding='SAME'))
+    l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1],              # l2 shape=(?, 7, 7, 64)
+                        strides=[1, 2, 2, 1], padding='SAME')
+    l2 = tf.nn.dropout(l2, p_keep_conv)
+
+    l3a = tf.nn.relu(tf.nn.conv2d(l2, w3,                     # l3a shape=(?, 7, 7, 128)
+                        strides=[1, 1, 1, 1], padding='SAME'))
+    l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1],              # l3 shape=(?, 4, 4, 128)
+                        strides=[1, 2, 2, 1], padding='SAME')
+    l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]])    # reshape to (?, 2048)
+    l3 = tf.nn.dropout(l3, p_keep_conv)
+
+    l4 = tf.nn.relu(tf.matmul(l3, w4))
+    l4 = tf.nn.dropout(l4, p_keep_hidden)
+
+    pyx = tf.matmul(l4, w_o)
+    return pyx
+
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
+trX = trX.reshape(-1, 28, 28, 1)  # 28x28x1 input img
+teX = teX.reshape(-1, 28, 28, 1)  # 28x28x1 input img
+
+X = tf.placeholder("float", [None, 28, 28, 1])
+Y = tf.placeholder("float", [None, 10])
+
+w = init_weights([3, 3, 1, 32])       # 3x3x1 conv, 32 outputs
+w2 = init_weights([3, 3, 32, 64])     # 3x3x32 conv, 64 outputs
+w3 = init_weights([3, 3, 64, 128])    # 3x3x32 conv, 128 outputs
+w4 = init_weights([128 * 4 * 4, 625]) # FC 128 * 4 * 4 inputs, 625 outputs
+w_o = init_weights([625, 10])         # FC 625 inputs, 10 outputs (labels)
+
+p_keep_conv = tf.placeholder("float")
+p_keep_hidden = tf.placeholder("float")
+py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)
+
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))
+train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
+predict_op = tf.argmax(py_x, 1)
+
+# Launch the graph in a session
+with tf.Session() as sess:
+    # you need to initialize all variables
+    tf.initialize_all_variables().run()
+
+    for i in range(50):
+        training_batch = zip(range(0, len(trX), batch_size),
+                             range(batch_size, len(trX)+1, batch_size))
+        for start, end in training_batch:
+            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],
+                                          p_keep_conv: 0.8, p_keep_hidden: 0.5})
+
+        test_indices = np.arange(len(teX)) # Get A Test Batch
+        np.random.shuffle(test_indices)
+        test_indices = test_indices[0:test_size]
+        w_out = sess.run(w)
+        
+        print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
+                         sess.run(predict_op, feed_dict={X: teX[test_indices],
+                                                         p_keep_conv: 1.0,
+                                                         p_keep_hidden: 1.0})))