深度学习入门实战_深度学习实战

1.DNN
1.1.DNN结构和数据说明


1.2 DNN代码实现

# -*- coding: utf-8 -*-
"""
Created on Tue May 28 19:22:44 2019

@author: lcl
"""

from __future__ import print_function

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf

# Parameters
learning_rate = 0.1
num_steps = 500
batch_size = 128
display_step = 100

# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)

# tf Graph input
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))
}
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([num_classes]))
}

# Create model
def neural_net(x):
    # Hidden fully connected layer with 256 neurons
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    # Hidden fully connected layer with 256 neurons
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    # Output fully connected layer with a neuron for each class
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer

logits = neural_net(X)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits,labels = Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)

correct_pred = tf.equal(tf.argmax(logits,1),tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(1,num_steps + 1):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
                                                                 Y: batch_y})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))
            
    print("Optimization Finished!")

    # Calculate accuracy for MNIST test images
    print("Testing Accuracy:", \
         sess.run(accuracy, feed_dict={X: mnist.test.images,
                                      Y: mnist.test.labels}))
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1.3运行结果

1.4 相关函数说明

2.CNN
2.1 CNN结构

2.2 CNN代码实现

# -*- coding: utf-8 -*-
"""
Created on Wed May 29 14:31:38 2019

@author: lcl
"""

from __future__ import division, print_function, absolute_import

import tensorflow as tf

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

#training parameters
learning_rate = 0.001
num_steps = 100
batch_size = 128
display_step = 10

#Network parameters
num_input = 784
num_classes = 10
dropout = 0.75

#tf graph input
X = tf.placeholder(tf.float32,[None,num_input])
Y = tf.placeholder(tf.float32,[None,num_classes])
keep_prob = tf.placeholder(tf.float32)

def conv2d(x,W,b,strides = 1):
    x = tf.nn.conv2d(x,W,strides = [1,strides,strides,1],padding = 'SAME')
    x = tf.nn.bias_add(x,b)
    return tf.nn.relu(x)

def maxpool2d(x,k=2):
    return tf.nn.max_pool(x,ksize = [1,k,k,1],strides = [1,k,k,1],padding = 'SAME')

def conv_net(x,weights,biases,dropout):
    x = tf.reshape(x,shape = [-1,28,28,1])
    
    conv1 = conv2d(x,weights['wc1'],biases['bc1'])
    conv1 = maxpool2d(conv1,k = 2)
    
    conv2 = conv2d(conv1,weights['wc2'],biases['bc2'])
    conv2 = maxpool2d(conv2,k = 2)
    
    fc1 = tf.reshape(conv2,[-1,weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1,weights['wd1']),biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    fc1 = tf.nn.dropout(fc1,dropout)
    
    out = tf.add(tf.matmul(fc1,weights['out']),biases['out'])
    return out


weights = {
        'wc1':tf.Variable(tf.random_normal([5,5,1,32])),
        'wc2':tf.Variable(tf.random_normal([5,5,32,64])),
        'wd1':tf.Variable(tf.random_normal([7*7*64,1024])),
        'out':tf.Variable(tf.random_normal([1024,num_classes]))
        }

biases = {
        'bc1':tf.Variable(tf.random_normal([32])),
        'bc2':tf.Variable(tf.random_normal([64])),
        'bd1':tf.Variable(tf.random_normal([1024])),
        'out':tf.Variable(tf.random_normal([num_classes]))
        }

logits = conv_net(X,weights,biases,keep_prob)
prediction = tf.nn.softmax(logits)

loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits,labels = Y))
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
train_op = optimizer.minimize(loss_op)

correct_pred = tf.equal(tf.argmax(prediction,1),tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    
    for step in range(1,num_steps+1):
        batch_x,batch_y = mnist.train.next_batch(batch_size)
        sess.run(train_op,feed_dict = {X:batch_x,Y:batch_y,keep_prob:dropout})
        if step % display_step == 0 or step == 1:
            loss,acc = sess.run([loss_op,accuracy],feed_dict = {X:batch_x,Y:batch_y,keep_prob:1.0})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))

    print("Optimization Finished!")  
    
    # Calculate accuracy for 256 MNIST test images
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: mnist.test.images[:256],
                                      Y: mnist.test.labels[:256],
                                      keep_prob: 1.0}))
   
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2.3 运行结果

2.4 参数说明
2828（原始图片大小) ---- 3030（padding）---- 2626（卷积）----- 2828（padding）----1414（pooling）----- 1616(padding) ----- 1212(卷积) ----- 1414（padding） ----- 7*7(pooling)
3 RNN
3.1 RNN结构

3.2 RNN实现代码

# -*- coding: utf-8 -*-
"""
Created on Wed May 29 16:05:54 2019

@author: admin
"""

from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib import rnn

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

tf.reset_default_graph()

#training parameters
learning_rate = 0.001
training_steps = 500
batch_size = 128
display_step = 20

#network parameters
num_input = 28
timesteps = 28
num_hidden = 128
num_classes = 10

#tf graph input
X = tf.placeholder("float",[None,timesteps,num_input])
Y = tf.placeholder("float",[None,num_classes])


#define weights
weights = {
        'out':tf.Variable(tf.random_normal([num_hidden,num_classes]))
        }

biases = {
        'out':tf.Variable(tf.random_normal([num_classes]))
        }

def RNN(x,weights,biases):
    x = tf.unstack(x,timesteps,1)
    
    lstm_cell = rnn.BasicLSTMCell(num_hidden,forget_bias = 1.0)
    
    #outputs,states = rnn.static_rnn(lstm_cell,x,dtype = tf.float32)
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
    
    return tf.matmul(outputs[-1],weights['out'] + biases['out'])

logits = RNN(X,weights,biases)
prediction = tf.nn.softmax(logits)

loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits,labels = Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate)
train_op = optimizer.minimize(loss_op)

correct_pred = tf.equal(tf.argmax(prediction,1),tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(1,training_steps + 1):
        batch_x,batch_y = mnist.train.next_batch(batch_size)
        batch_x = batch_x.reshape(batch_size,timesteps,num_input)
        sess.run(train_op,feed_dict = {X:batch_x,Y:batch_y})
        if step % display_step == 0 or step == 1:
            loss,acc = sess.run([loss_op,accuracy],feed_dict = {X:batch_x,Y:batch_y})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))
            
    print("Optimization Finished!")
    
    # Calculate accuracy for 128 mnist test images
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))
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3.3 输出结果

3.4参数说明
xi为28*28矩阵的第i列，RNN中每次输入矩阵中的一列，共输入28次

4.参考
https://github.com/aymericdamien/TensorFlow-Examples