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import tensorflow as tf
sess = tf.Session()

from keras import backend as K
K.set_session(sess)


    

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# this placeholder will contain our input digits, as flat vectors
img = tf.placeholder(tf.float32, shape=(None, 784))


    

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from keras.layers import Dense

# Keras layers can be called on TensorFlow tensors:
x = Dense(128, activation='relu')(img)  # fully-connected layer with 128 units and ReLU activation
x = Dense(128, activation='relu')(x)
preds = Dense(10, activation='softmax')(x)  # output layer with 10 units and a softmax activation


    

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labels = tf.placeholder(tf.float32, shape=(None, 10))

from keras.objectives import categorical_crossentropy
loss = tf.reduce_mean(categorical_crossentropy(labels, preds))


    

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from tensorflow.examples.tutorials.mnist import input_data
mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
with sess.as_default():
    for i in range(100):
        batch = mnist_data.train.next_batch(50)
        train_step.run(feed_dict={img: batch[0],
                                  labels: batch[1]})


    

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mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)


    

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