神經/卷積神經網絡模型架構

#構造神經網絡框架：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data

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

n_hidden_1 = 256
n_hidden_2 = 128
n_input = 784
n_classes = 10

x = tf.placeholder("float", [None,n_input])
y = tf.placeholder("float", [None,n_classes])

stddev = 0.1
weights = {
    'w1':tf.Variable( tf.random_normal([n_input, n_hidden1],stddev =stddev)),
    'w2':tf.Variable( tf.random_normal([n_hidden_1, n_hidden_2],stddev=stddev)),
    'out':tf.Variable( tf.random_normal([n_hidden_2,n_classes],stddev=stddev))
}
biases = {
    'b1':tf.Variable(tf.random_normal([n_hidden_1])),
    'b2':tf.Variable(tf.random_normal([n_hidden_2])),
    'out':tf.Variable(tf.random_normal([n_classes]))
}
print("NETWORK READY")

#使用tensorflow訓練神經網絡

def multilayer_perceptron(_X，_weights, _biases):
    layer_1 = tf.nn.sigmod(tf.add(tf.matual(_X, _weights['w1']), _biases['b1']))
    layer_2 = tf.nn.sigmod(tf.add(tf.matual(layer_1,_weights['w2']), _biases['b2']))
    return(tf.matual(layer_2), _weights['out'] + _biases['out'])

pred = multilayer_perceptron(x,weights,biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost)
corr = tf.equal(tf.argmax(pred,1),  tf.argmax(y,1))     //準確率
accr = tf.reduce_mean(tf.cast(corr,"float"))

init = tf.global_variables_initializer()
print("FUNCTIONS READY")

training_epochs = 20
batch_size = 100
display_step = 4

sess = tf.Session()
sess.run(init)

for epoch in range(training_epochs):
    avg_cost = 0
    total_batch = int(mnist.train.num_examples/batch_size)
    #iteration
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feeds = {x:batch_xs, y:batch_ys}
        sess.run(optm, feed_dict=feeds)
        avg_cost +=sess.run(cost, feed_dict=feeds)
    avg_cost = avg_cost / total_batch
    #DISPLAY
    if (ephch+1)% display_step == 0:
        print("Epoch:%03d/%03d cost:%.9f"%(epoch, training_epochs, avg_cost))
        feeds = {x:batch_xs, y:batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print("TRAIN ACCURACY:%.3f" %(train_acc))
        feeds = {x:mnist.test.images, y:mnist.test.labels}
        test_acc = sess.run(accr, feed_dict=feeds)
        print("REST ACCURACY:%.3f"%(test_acc))
print("OPTIMIZATION FINISHED")

卷積神經網絡：

n_input = 784
n_output = 10
weights = {
     'wc1':tf.Variable(tf.random_normal([3,3,1,64], stddev=0.1)),
     'wc2':tf.Variable(tf.random_normal([3,3,64,128],stddev=0.1)),
     'wd1':tf.Variable(tf.random_normal([7*7*128,1024],stddev=0.1)),
     'wd2':tf.Variable(tf.random_normal([1024,n_output],stddev=0.1))
}
biases= {
    'bc1':tf.Variable(tf.random_normal([64], stddev=0.1)),
    'bc2':tf.Variable(tf.random_normal([128],stddev=0.1)),
    'bd1':tf.Variable(tf.random_normal([1024],stddev=0.1)),
    'bd2':tf.Variable(tf.random-normal([n_output],stddev=0.1))
}