<reponame>manailin/tensorflow___examples
#!/usr/bin/env python

import numpy as np
import tensorflow as tf

import input_data


def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))


def model(X, w_h, w_o):
    h = tf.nn.sigmoid(tf.matmul(
        X, w_h))  # this is a basic mlp, think 2 stacked logistic regressions
    return tf.matmul(
        h,
        w_o)  # note that we dont take the softmax at the end because our cost fn does that for us


mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])

w_h = init_weights([784, 625])  # create symbolic variables
w_o = init_weights([625, 10])

py_x = model(X, w_h, w_o)

cost = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(py_x, Y))  # compute costs
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(
    cost)  # construct an optimizer
predict_op = tf.argmax(py_x, 1)

# Launch the graph in a session
with tf.Session() as sess:
    # you need to initialize all variables
    tf.initialize_all_variables().run()

    for i in range(100):
        for start, end in zip(
                range(0, len(trX), 128), range(128, len(trX), 128)):
            sess.run(train_op,
                     feed_dict={X: trX[start:end],
                                Y: trY[start:end]})
        print(i,
              np.mean(np.argmax(teY, axis=1) == sess.run(predict_op,
                                                         feed_dict={X: teX,
                                                                    Y: teY})))

#!/usr/bin/env python

import numpy as np
import tensorflow as tf

import input_data


def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))


def model(X, w_h, w_o):
    h = tf.nn.sigmoid(tf.matmul(
        X, w_h))  # this is a basic mlp, think 2 stacked logistic regressions
    return tf.matmul(
        h,
        w_o)  # note that we dont take the softmax at the end because our cost fn does that for us


mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])

w_h = init_weights([784, 625])  # create symbolic variables
w_o = init_weights([625, 10])

py_x = model(X, w_h, w_o)

cost = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(py_x, Y))  # compute costs
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(
    cost)  # construct an optimizer
predict_op = tf.argmax(py_x, 1)

# Launch the graph in a session
with tf.Session() as sess:
    # you need to initialize all variables
    tf.initialize_all_variables().run()

    for i in range(100):
        for start, end in zip(
                range(0, len(trX), 128), range(128, len(trX), 128)):
            sess.run(train_op,
                     feed_dict={X: trX[start:end],
                                Y: trY[start:end]})
        print(i,
              np.mean(np.argmax(teY, axis=1) == sess.run(predict_op,
                                                         feed_dict={X: teX,
                                                                    Y: teY})))
#!/usr/bin/env python # this is a basic mlp, think 2 stacked logistic regressions # note that we dont take the softmax at the end because our cost fn does that for us # create symbolic variables # compute costs # construct an optimizer # Launch the graph in a session # you need to initialize all variables