56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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n_nodes_hl1 = 500
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n_nodes_hl2 = 500
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n_nodes_hl3 = 500
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n_classes = 10
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batch_size = 100
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x = tf.placeholder('float', [None, 784])
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y = tf.placeholder('float')
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def neural_network_model(data):
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hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])), 'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
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hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
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hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
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output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 'biases': tf.Variable(tf.random_normal([n_classes]))}
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l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
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l1 = tf.nn.relu(l1)
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l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
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l2 = tf.nn.relu(l2)
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l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])
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l3 = tf.nn.relu(l3)
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output = tf.add(tf.matmul(l3, output_layer['weights']),
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output_layer['biases'])
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return output
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def train_neural_network(x):
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prediction = neural_network_model(x)
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cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
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optimizer = tf.train.AdamOptimizer().minimize(cost)
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hm_epochs = 10
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for epoch in range(hm_epochs):
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epoch_loss = 0
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for _ in range(int(mnist.train.num_examples / batch_size)):
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epoch_x, epoch_y = mnist.train.next_batch(batch_size)
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_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
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epoch_loss += c
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print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:',
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epoch_loss)
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correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
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accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
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print('Accuracy:', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
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train_neural_network(x)
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