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TensorFlow tutorial: Small CNN to process MNIST data
# From instructions at https://www.tensorflow.org/versions/r1.0/get_started/mnist/pros
import argparse
import sys
import tensorflow as tf
import time
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = None
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir)
# ---------------- MODEL DEF -------------------
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.int64, [None])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# --- 1 --- define our first convolutional layer
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
# image is now 14x14
# --- 2 --- DEFINE 2nd convolutional layer
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+ b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
# image is now 7x7
# --- 3 --- DENSELY CONNECTED LAYER
# fully connected, 1024 neurons
W_fc1 = weight_variable([7*7*64, 1024])
# input is 7x7 x 64 channels
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# --- 4 --- DROPOUT
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# --- 5 --- READOUT LAYER: readout to our 10 values in the one-hot label
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# --------------- TRAINING STEPS -----------------
cross_entropy = tf.reduce_mean(tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# evaluating our model
correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# now run it!
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# initialize all variables
tf.global_variables_initializer().run()
# run training step many times!
start = time.time()
for i in range(1000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_:batch[1], keep_prob: 1.0})
print("Step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x:batch[0], y_:batch[1], keep_prob: 0.5})
end = time.time()
# print how well the model does on the test data
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0
}))
print("Training time: {} sec".format(end - start))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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