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- import tensorflow as tf
- from tensorflow.examples.tutorials.mnist import input_data
- '''
- 1. first feed forward the data:
- input -> weights -> hidden layer 1 -> activation ->
- weights -> hidden layer 2 -> activation ->
- weights -> output layer
- 2. Compute cost function (cross entropy) to figure out how close we are
- 3. Minimize cost using optimizer function (AdamOptimizer, SGD, AdaGrad, etc.).
- '''
- mnist = input_data.read_data_sets("./temp/data", one_hot=True)
- n_nodes_hl1 = 500
- n_nodes_hl2 = 500
- n_nodes_hl3 = 500
- n_classes = 10
- batch_size = 100
- x = tf.placeholder('float', [None, 784])
- y = tf.placeholder('float')
- def neural_network_model(data):
- hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])),
- 'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
- hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
- 'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
- hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
- 'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
- output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
- 'biases': tf.Variable(tf.random_normal([n_classes]))}
- l1 = tf.add(tf.matmul(data, hidden_1_layer[
- 'weights']), hidden_1_layer['biases'])
- l1 = tf.nn.relu(l1)
- l2 = tf.add(tf.matmul(l1, hidden_2_layer[
- 'weights']), hidden_2_layer['biases'])
- l2 = tf.nn.relu(l2)
- l3 = tf.add(tf.matmul(l2, hidden_3_layer[
- 'weights']), hidden_3_layer['biases'])
- l3 = tf.nn.relu(l3)
- output = tf.add(tf.matmul(l3, output_layer[
- 'weights']), output_layer['biases'])
- return output
- def train_neural_network(x):
- prediction = neural_network_model(x)
- cost = tf.reduce_mean(
- tf.nn.softmax_cross_entropy_with_logits(prediction, y))
- optimizer = tf.train.AdamOptimizer().minimize(cost)
- hm_epochs = 10
- with tf.Session() as sess:
- sess.run(tf.initialize_all_variables())
- for epoch in range(hm_epochs):
- epoch_loss = 0
- for _ in range(int(mnist.train.num_examples / batch_size)):
- epoch_x, epoch_y = mnist.train.next_batch(batch_size)
- _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
- epoch_loss += c
- print('epoch', epoch, 'completed out of',
- hm_epochs, 'loss:', epoch_loss)
- correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
- accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
- print('accuracy:', accuracy.eval(
- {x: mnist.test.images, y: mnist.test.labels}))
- train_neural_network(x)
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