使用二维数据构造简单卷积神经网络 觉得有用的话,欢迎一起讨论相互学习~Follow Me
图像和一些时序数据集都可以用二维数据的形式表现，我们此次使用随机分布的二位数据构造一个简单的CNN—网络卷积-最大池化-全连接
参考代码# Implementing Different Layers # --------------------------------------- # # We will illustrate how to use different types # of layers in TensorFlow # # The layers of interest are: # (1) Convolutional Layer卷积层 # (2) Activation Layer激活层 # (3) Max-Pool Layer池化层 # (4) Fully Connected Layer 全连接层 # # We will generate two different data sets for this # script, a 1-D data set (row of data) and # a 2-D data set (similar to picture) import tensorflow as tf import matplotlib.pyplot as plt import csv import os import random import numpy as np import random from tensorflow.python.framework import ops # ---------------------------------------------------| # -------------------2D-data-------------------------| # ---------------------------------------------------| # Reset Graph 重置图模型 ops.reset_default_graph() sess = tf.Session() # parameters for the run 运行参数 row_size = 10 # 2D图形高 col_size = 10 # 2D图形长 conv_size = 2 conv_stride_size = 2 # 卷积步长 maxpool_size = 2 maxpool_stride_size = 1 # 池化步长 # ensure reproducibility 确保复现性 seed = 13 np.random.seed(seed) tf.set_random_seed(seed) # Generate 2D data生成随机二维数据 data_size = [row_size, col_size] data_2d = np.random.normal(size=data_size) # --------Placeholder-------- x_input_2d = tf.placeholder(dtype=tf.float32, shape=data_size) # Convolution 卷积层 def conv_layer_2d(input_2d, my_filter, stride_size): # TensorFlow's 'conv2d()' function only works with 4D arrays: # [batch, height, width, channels], we have 1 batch, and # 1 channel, but we do have width AND height this time. # So next we create the 4D array by inserting dimension 1's. # Tensorflow的卷积操作默认输入有四个维度[batch_size, height, width, channels] # 此处我们将维度增加到四维 input_3d = tf.expand_dims(input_2d, 0) input_4d = tf.expand_dims(input_3d, 3) convolution_output = tf.nn.conv2d(input_4d, filter=my_filter, strides=[1, stride_size, stride_size, 1], padding="VALID") # filter表示卷积核，数据维度为[卷积核高度，卷积核长度，输入通道数，输出通道数] # stride_size表示步长，数据维度为[批处理数据大小,步长高，步长宽，通道数]，其中批处理数据大小和通道数一般跨度都为1，不需要修改。 # Get rid of unnecessary dimensions # 将维数为1的维度去掉，保留数值数据。 conv_output_2d = tf.squeeze(convolution_output) return (conv_output_2d) # Create Convolutional Filter my_filter = tf.Variable(tf.random_normal(shape=[conv_size, conv_size, 1, 1])) # Create Convolutional Layer my_convolution_output = conv_layer_2d(x_input_2d, my_filter, stride_size=conv_stride_size) # --------Activation-------- def activation(input_1d): return (tf.nn.relu(input_1d)) # Create Activation Layer 激活层 my_activation_output = activation(my_convolution_output) # --------Max Pool-------- def max_pool(input_2d, width, height, stride): # Just like 'conv2d()' above, max_pool() works with 4D arrays. # [batch_size=1, height=given, width=given, channels=1] input_3d = tf.expand_dims(input_2d, 0) input_4d = tf.expand_dims(input_3d, 3) # Perform the max pooling with strides = [1,1,1,1] # If we wanted to increase the stride on our data dimension, say by # a factor of '2', we put strides = [1, 2, 2, 1] pool_output = tf.nn.max_pool(input_4d, ksize=[1, height, width, 1], strides=[1, stride, stride, 1], padding='VALID') # Get rid of unnecessary dimensions pool_output_2d = tf.squeeze(pool_output) return (pool_output_2d) # Create Max-Pool Layer 最大池化层 # 即选择窗口中的最大值作为输出，池化层的窗口格式定义和卷积核的不一样 # 其四维数组格式定义和卷积步长，池化步长一致，和输入格式相同，即 # [batch_size, height, width, channels] my_maxpool_output = max_pool(my_activation_output, width=maxpool_size, height=maxpool_size, stride=maxpool_stride_size) # --------Fully Connected-------- def fully_connected(input_layer, num_outputs): # 扁平化/光栅化处理使最大池化层输出为一维向量形式 flat_input = tf.reshape(input_layer, [-1]) # 设定weight的形状 weight_shape = tf.squeeze(tf.stack([tf.shape(flat_input), [num_outputs]])) # 初始化weight weight = tf.random_normal(weight_shape, stddev=0.1) # 初始化bias bias = tf.random_normal(shape=[num_outputs]) # 将一维输入还原为二维 input_2d = tf.expand_dims(flat_input, 0) # 计算输出 full_output = tf.add(tf.matmul(input_2d, weight), bias) # 降维，去掉维度为1的维度，便于进行观察 full_output_2d = tf.squeeze(full_output) return (full_output_2d) # Create Fully Connected Layer my_full_output = fully_connected(my_maxpool_output, 5) # Run graph # Initialize Variables init = tf.global_variables_initializer() sess.run(init) feed_dict = {x_input_2d: data_2d} print('\n>>>> 2D Data <<<<') # Convolution Output print('Input = %s array'%(x_input_2d.shape.as_list())) print('%s Convolution, stride size = [%d, %d] , results in the %s array'% (my_filter.get_shape().as_list()[:2], conv_stride_size, conv_stride_size, my_convolution_output.shape.as_list())) print(sess.run(my_convolution_output, feed_dict=feed_dict)) # Activation Output print('\nInput = the above %s array'%(my_convolution_output.shape.as_list())) print('ReLU element wise returns the %s array'%(my_activation_output.shape.as_list())) print(sess.run(my_activation_output, feed_dict=feed_dict)) # Max Pool Output print('\nInput = the above %s array'%(my_activation_output.shape.as_list())) print('MaxPool, stride size = [%d, %d], results in %s array'% (maxpool_stride_size, maxpool_stride_size, my_maxpool_output.shape.as_list())) print(sess.run(my_maxpool_output, feed_dict=feed_dict)) # Fully Connected Output 全连接层 print('\nInput = the above %s array'%(my_maxpool_output.shape.as_list())) # 全连接层输出除掉batch_size和channel维度后剩余维度为[4,4] print('Fully connected layer on all %d rows results in %s outputs:'% (my_maxpool_output.shape.as_list()[0], my_full_output.shape.as_list()[0])) # 由于全连接层神经元为5个所以输出维度为5 print(sess.run(my_full_output, feed_dict=feed_dict)) '''对于卷积层输出维度[batch_size,output_size,output_size',channels] 其中output_size及output_size'表示为对应维度上通过(W-F+2P)/S+1得到的结果 W为数据维度，F为卷积核或池化窗口的宽或高，P为Padding大小，其中设置卷积为Valid时，Padding为0若设置为SAME卷积，则会有Padding，S是步长大小 本例子中卷积层计算公式为[(10-2)+0]/2+1=5，池化层计算公式为[(5-2)+0]/1+1=4''' # >>>> 2D Data <<<< # Input = [10, 10] array # [2, 2] Convolution, stride size = [2, 2] , results in the [5, 5] array # [[ 0.14431179 0.72783369 1.51149166 -1.28099763 1.78439188] # [-2.54503059 0.76156765 -0.51650006 0.77131093 0.37542343] # [ 0.49345911 0.01592223 0.38653135 -1.47997665 0.6952765 ] # [-0.34617192 -2.53189754 -0.9525758 -1.4357065 0.66257358] # [-1.98540258 0.34398788 2.53760481 -0.86784822 -0.3100495 ]] # # Input = the above [5, 5] array # ReLU element wise returns the [5, 5] array # [[ 0.14431179 0.72783369 1.51149166 0. 1.78439188] # [ 0. 0.76156765 0. 0.77131093 0.37542343] # [ 0.49345911 0.01592223 0.38653135 0. 0.6952765 ] # [ 0. 0. 0. 0. 0.66257358] # [ 0. 0.34398788 2.53760481 0. 0. ]] # # Input = the above [5, 5] array # MaxPool, stride size = [1, 1], results in [4, 4] array # [[ 0.76156765 1.51149166 1.51149166 1.78439188] # [ 0.76156765 0.76156765 0.77131093 0.77131093] # [ 0.49345911 0.38653135 0.38653135 0.6952765 ] # [ 0.34398788 2.53760481 2.53760481 0.66257358]] # # Input = the above [4, 4] array # Fully connected layer on all 4 rows results in 5 outputs: # [ 0.08245847 -0.16351229 -0.55429065 -0.24322605 -0.99900764]