60 lines
2.3 KiB
Python
60 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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def neural_network_model(data):
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hidden_1_layer={"weights":tf.Variable(tf.random_normal([784,n_nodes_hl1])),
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"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])),
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"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])),
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"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])),
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"biases":tf.Variable(tf.random_normal([n_classes]))}
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l1=tf.add(tf.matmultiply(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.matmultiply(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.matmultiply(l2,hidden_3_layer["weights"]), hidden_3_layer["biases"])
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l3=tf.nn.relu(l3)
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output=tf.matmultiply(l3,output_layer["weights"]) + 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(prediction,y))
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#default learning_rate=0.001
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optimier=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.initialize_all_variables())
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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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x,y=mnist.train.next_batch(batch_size)
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_,c=sess.run([optimizer,cost],feed_dict={x:x,y:y})
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epoch_loss+=c
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print("Epoch: ",epoch,"completed out of",hm_epochs,"loss:",epoch_loss)
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correct=tf.equals(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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