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deep.py
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deep.py
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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import pickle
mnist = input_data.read_data_sets("/tmp/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]))}
# (data * weights) + biases
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.matmul(l3, output_layer['weights']) + output_layer['biases']
return output
mnist_feature_1 = None
def train_neural_network(x):
prediction = neural_network_model(x)
print('Model init')
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = prediction))
# learning_rate = 0.001
optimiser = tf.train.AdamOptimizer().minimize(cost)
# cycles feed forward + backpropagation
hm_epochs = 10
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
# Training
for epoch in range(hm_epochs):
print(epoch)
if epoch != 0:
saver.restore(sess, "/Pro/tf/model/mnist_model.ckpt")
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([optimiser, cost], feed_dict={x:epoch_x, y:epoch_y})
epoch_loss += c
p = saver.save(sess, "/Pro/tf/model/mnist_model.ckpt")
print('Epoch', epoch, 'completed out of ', hm_epochs, 'loss: ', epoch_loss, 'model: ', p)
print(prediction.eval({x:[epoch_x[0]]}), y.eval({y: [epoch_y[0]]}))
print(tf.argmax(prediction, 1).eval({x:[epoch_x[0]]}), tf.argmax(y, 1).eval({y: [epoch_y[0]]}))
print(tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)).eval( {x:[epoch_x[0]], y: [epoch_y[0]]}))
print(tf.cast(tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)), 'float').eval( {x:[epoch_x[0]], y: [epoch_y[0]]}))
print(tf.reduce_mean(tf.cast(tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)), 'float')).eval( {x:[epoch_x[0]], y: [epoch_y[0]]}))
print(tf.cast(tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)), 'float').eval( {x:mnist.test.images, y: mnist.test.labels}))
mnist_feature_1 = [epoch_x, epoch_y]
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}))
with open('model/mnist_feature.pickle', 'wb') as f:
pickle.dump(mnist_feature_1, f)
train_neural_network(x)