# coding: utf-8

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from tensorflow.examples.tutorials.mnist import input_data


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mnist = input_data.read_data_sets('./mnist_data', one_hot=True)


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xtrain,xtest = mnist.train,mnist.test
import tensorflow as tf
# mnist.train.


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learning_rate = tf.constant(0.01,name='learning_rate')
xtrain.images.shape[1]


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x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])


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W1 = tf.Variable(tf.zeros([784, 512]),name='layer_1_weights')
b1 = tf.Variable(tf.zeros([512]),name='bias_1_weights')
W2 = tf.Variable(tf.zeros([512, 128]),name='layer_2_weights')
b2 = tf.Variable(tf.zeros([128]),name='bias_2_weights')
W_o = tf.Variable(tf.zeros([128, 10]),name='layer_output_weights')
b_o = tf.Variable(tf.zeros([10]),name='bias_output_weights')


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layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,W2),b2))
output_layer = tf.nn.softmax(tf.add(tf.matmul(layer_2,W_o),b_o))


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cross_entropy = tf.reduce_mean(-tf.reduce_sum(output_layer * tf.log(y), reduction_indices=[1]))


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train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)


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with tf.Session() as s:
    tf.global_variables_initializer()
    [_,val] = s.run([train_step,cross_entropy],feed_dict={x:xtrain.images,y:xtrain.labels})
    


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