import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\\F\\TensorFlow_deep_learn\\MNIST\\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])

weight = tf.Variable(tf.ones([784, 10]))

bias = tf.Variable(tf.ones([10]))

y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)

y_data = tf.placeholder("float32", [None, 10])

loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for _ in range(1000):

    batch_xs, batch_ys = mnist.train.next_batch(100)

    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})

    if _ % 50 == 0:

        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\\F\\TensorFlow_deep_learn\\MNIST\\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])

weight = tf.Variable(tf.ones([784, 10]))

bias = tf.Variable(tf.ones([10]))

y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)

y_data = tf.placeholder("float32", [None, 10])

loss = -tf.reduce_sum(y_data*tf.log(y_model))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for _ in range(1000):

    batch_xs, batch_ys = mnist.train.next_batch(50)

    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})

    if _ % 50 == 0:

        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\\F\\TensorFlow_deep_learn\\MNIST\\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])

weight1 = tf.Variable(tf.ones([784, 256]))

bias1 = tf.Variable(tf.ones([256]))

y1_model1 = tf.matmul(x_data, weight1) + bias1

weight2 = tf.Variable(tf.ones([256, 10]))

bias2 = tf.Variable(tf.ones([10]))

y_model = tf.nn.softmax(tf.matmul(y1_model1, weight2) + bias2)

y_data = tf.placeholder("float32", [None, 10])

loss = -tf.reduce_sum(y_data*tf.log(y_model))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for _ in range(1000):

    batch_xs, batch_ys = mnist.train.next_batch(50)

    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})

    if _ % 50 == 0:

        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\\F\\TensorFlow_deep_learn\\MNIST\\", one_hot=True)

x_data = tf.placeholder("float32", [None, 784])

x_image = tf.reshape(x_data, [-1,28,28,1])

w_conv = tf.Variable(tf.ones([5,5,1,32]))

b_conv = tf.Variable(tf.ones([32]))

h_conv = tf.nn.relu(tf.nn.conv2d(x_image, w_conv, strides=[1, 1, 1, 1], padding='SAME') + b_conv)

h_pool = tf.nn.max_pool(h_conv, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

w_fc = tf.Variable(tf.ones([14*14*32,1024]))

b_fc = tf.Variable(tf.ones([1024]))

h_pool_flat = tf.reshape(h_pool, [-1, 14*14*32])

h_fc = tf.nn.relu(tf.matmul(h_pool_flat, w_fc) + b_fc)

W_fc2 = tf.Variable(tf.ones([1024,10]))

b_fc2 = tf.Variable(tf.ones([10]))

y_model = tf.nn.softmax(tf.matmul(h_fc, W_fc2) + b_fc2)

y_data = tf.placeholder("float32", [None, 10])

loss = -tf.reduce_sum(y_data*tf.log(y_model))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for _ in range(1000):

    batch_xs, batch_ys = mnist.train.next_batch(200)

    sess.run(train_step, feed_dict={x_data:batch_xs, y_data:batch_ys})

    if _ % 50 == 0:

        correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print(sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))

import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("D:\\F\\TensorFlow_deep_learn\\MNIST\\", one_hot=True)

x_data = tf.placeholder("float", shape=[None, 784])

y_data = tf.placeholder("float", shape=[None, 10])

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='VALID')

def max_pool_2x2(x):

    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

W_conv1 = weight_variable([5, 5, 1, 32])

b_conv1 = bias_variable([32])

x_image = tf.reshape(x_data, [-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([4 * 4 * 64, 1024])

b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 4*4*64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_data * tf.log(y_conv))

train_step = tf.train.AdamOptimizer(1e-2).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.Session()

sess.run(tf.initialize_all_variables())

for i in range(1000):

    batch = mnist.train.next_batch(50)

    if i%5 == 0:

        train_accuracy = sess.run(accuracy, feed_dict={x_data:batch[0], y_data: batch[1], keep_prob: 1.0})

        print("step %d, training accuracy %g"%(i, train_accuracy))

    sess.run(train_step, feed_dict={x_data: batch[0], y_data: batch[1], keep_prob: 0.5})