<jupyter_start><jupyter_text>Load our sample data as one-hot vectors<jupyter_code>from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)<jupyter_output>Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz<jupyter_text>Start an interactive session, not sure why it's interactive<jupyter_code>sess = tf.InteractiveSession()<jupyter_output><empty_output><jupyter_text>Our input and output placeholders. The shapes start with `None` so we can batch multiple inputs<jupyter_code>x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])<jupyter_output><empty_output><jupyter_text>Make one layer of weights, plus bias<jupyter_code>W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))<jupyter_output><empty_output><jupyter_text>Our basic model is $\vec{x}\mathbf{W} + b = \vec{y}$<jupyter_code>y = tf.matmul(x, W) + b<jupyter_output><empty_output><jupyter_text>Optimize for cross-entropy<jupyter_code>cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
)<jupyter_output><empty_output><jupyter_text>At each training interval, do one gradient descent step to minimize cross entropy<jupyter_code>train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)<jupyter_output><empty_output><jupyter_text>Must initialize our session<jupyter_code>tf.global_variables_initializer().run()<jupyter_output><empty_output><jupyter_text>Our MNINST data type has a "next batch" that will deliver, say, 100 records at a time<jupyter_code>for _ in range(1000):
  batch = mnist.train.next_batch(100)
  train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))<jupyter_output>0.9173<jupyter_text>Convolutional Neural Network<jupyter_code>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')
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)
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)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
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)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(200):
        batch = mnist.train.next_batch(50)
        if i % 1000 == 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})

    print('test accuracy %g' % accuracy.eval(feed_dict={
          x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
sess = tf.Session()
type(sess)
sess.run(tf.global_variables_initializer())
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 1000 == 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})

print('test accuracy %g' % accuracy.eval(feed_dict={
      x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
mninst = input_data.read_data_sets('MNINST_data', one_hot=True)
batch = mninst.train.next_batch(50)
x
y_
keep_prob
train_accuracy = accuracy.eval(
    feed_dict={
        x: batch[0],
        y_: batch[1],
        keep_prob: 1.0
    }
)<jupyter_output><empty_output>
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