使用一维数据构造简单卷积神经网络
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神经网络对于一维数据非常重要，时序数据集、信号处理数据集和一些文本嵌入数据集都是一维数据，会频繁的使用到神经网络。我们在此利用一组一维数据构造卷积层-最大池化层-全连接层的卷积神经网络。希望给大家使用CNN处理一维数据一些帮助。
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 ops.reset_default_graph() # ---------------------------------------------------| # -------------------1D-data-------------------------| # ---------------------------------------------------| # Create graph session 创建初始图结构 ops.reset_default_graph() sess = tf.Session() # parameters for the run运行参数 data_size = 25 conv_size = 5 # 卷积核宽度方向的大小 maxpool_size = 5 # 池化层核宽度方向上的大小 stride_size = 1 # 卷积核宽度方向上的步长 # ensure reproducibility 确保复现性 seed = 13 np.random.seed(seed) tf.set_random_seed(seed) # Generate 1D data 生成一维数据 data_1d = np.random.normal(size=data_size) # Placeholder x_input_1d = tf.placeholder(dtype=tf.float32, shape=[data_size]) # --------Convolution-------- def conv_layer_1d(input_1d, my_filter, stride): # TensorFlow's 'conv2d()' function only works with 4D arrays: # [batch, height, width, channels], we have 1 batch, and # width = 1, but height = the length of the input, and 1 channel. # So next we create the 4D array by inserting dimension 1's. # 关于数据维度的处理十分关键，因为tensorflow中卷积操作只支持四维的张量， # 所以要人为的把数据补充为4维数据[1,1,25,1] input_2d = tf.expand_dims(input_1d, 0) input_3d = tf.expand_dims(input_2d, 0) input_4d = tf.expand_dims(input_3d, 3) # Perform convolution with stride = 1, if we wanted to increase the stride, # to say '2', then strides=[1,1,2,1] convolution_output = tf.nn.conv2d(input_4d, filter=my_filter, strides=[1, 1, stride, 1], padding="VALID") # Get rid of extra dimensions 去掉多余的层数，只保留数字 conv_output_1d = tf.squeeze(convolution_output) return (conv_output_1d) # Create filter for convolution. my_filter = tf.Variable(tf.random_normal(shape=[1, conv_size, 1, 1])) # Create convolution layer my_convolution_output = conv_layer_1d(x_input_1d, my_filter, stride=stride_size) # --------Activation-------- def activation(input_1d): return (tf.nn.relu(input_1d)) # Create activation layer my_activation_output = activation(my_convolution_output) # --------Max Pool-------- def max_pool(input_1d, width, stride): # Just like 'conv2d()' above, max_pool() works with 4D arrays. # [batch_size=1, width=1, height=num_input, channels=1] # 因为在处理卷积层的结果时，使用squeeze函数对结果输出进行降维，所以此处要将最大池化层的维度提升为4维 input_2d = tf.expand_dims(input_1d, 0) 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, 1, 2, 1] # We will also need to specify the width of the max-window ('width') pool_output = tf.nn.max_pool(input_4d, ksize=[1, 1, width, 1], strides=[1, 1, stride, 1], padding='VALID') # Get rid of extra dimensions pool_output_1d = tf.squeeze(pool_output) return (pool_output_1d) my_maxpool_output = max_pool(my_activation_output, width=maxpool_size, stride=stride_size) # --------Fully Connected-------- def fully_connected(input_layer, num_outputs): # First we find the needed shape of the multiplication weight matrix: # The dimension will be (length of input) by (num_outputs) weight_shape = tf.squeeze(tf.stack([tf.shape(input_layer), [num_outputs]])) # squeeze函数用于去掉维度为1的维度。保留数据。 # Initialize such weight # 初始化weight weight = tf.random_normal(weight_shape, stddev=0.1) # Initialize the bias # 初始化bias bias = tf.random_normal(shape=[num_outputs]) # Make the 1D input array into a 2D array for matrix multiplication # 将一维的数组添加一维成为2维数组 input_layer_2d = tf.expand_dims(input_layer, 0) # Perform the matrix multiplication and add the bias full_output = tf.add(tf.matmul(input_layer_2d, weight), bias) # Get rid of extra dimensions # 去掉多余的维度只保留数据 full_output_1d = tf.squeeze(full_output) return (full_output_1d) 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_1d: data_1d} print('>>>> 1D Data <<<<') # Convolution Output print('Input = array of length %d'%(x_input_1d.shape.as_list()[0])) # 25 print('Convolution w/ filter, length = %d, stride size = %d, results in an array of length %d:'% (conv_size, stride_size, my_convolution_output.shape.as_list()[0])) # 21 print(sess.run(my_convolution_output, feed_dict=feed_dict)) # Activation Output print('\nInput = above array of length %d'%(my_convolution_output.shape.as_list()[0])) # 21 print('ReLU element wise returns an array of length %d:'%(my_activation_output.shape.as_list()[0])) # 21 print(sess.run(my_activation_output, feed_dict=feed_dict)) # Max Pool Output print('\nInput = above array of length %d'%(my_activation_output.shape.as_list()[0])) # 21 print('MaxPool, window length = %d, stride size = %d, results in the array of length %d'% (maxpool_size, stride_size, my_maxpool_output.shape.as_list()[0])) # 17 print(sess.run(my_maxpool_output, feed_dict=feed_dict)) # Fully Connected Output print('\nInput = above array of length %d'%(my_maxpool_output.shape.as_list()[0])) # 17 print('Fully connected layer on all 4 rows with %d outputs:'% (my_full_output.shape.as_list()[0])) # 5 print(sess.run(my_full_output, feed_dict=feed_dict)) # >>>> 1D Data <<<< # Input = array of length 25 # Convolution w/ filter, length = 5, stride size = 1, results in an array of length 21: # [-2.63576341 -1.11550486 -0.95571411 -1.69670296 -0.35699379 0.62266493 # 4.43316031 2.01364899 1.33044648 -2.30629659 -0.82916248 -2.63594174 # 0.76669347 -2.46465087 -2.2855041 1.49780679 1.6960566 1.48557389 # -2.79799461 1.18149185 1.42146575] # # Input = above array of length 21 # ReLU element wise returns an array of length 21: # [ 0. 0. 0. 0. 0. 0.62266493 # 4.43316031 2.01364899 1.33044648 0. 0. 0. # 0.76669347 0. 0. 1.49780679 1.6960566 1.48557389 # 0. 1.18149185 1.42146575] # # Input = above array of length 21 # MaxPool, window length = 5, stride size = 1, results in the array of length 17 # [ 0. 0.62266493 4.43316031 4.43316031 4.43316031 4.43316031 # 4.43316031 2.01364899 1.33044648 0.76669347 0.76669347 1.49780679 # 1.6960566 1.6960566 1.6960566 1.6960566 1.6960566 ] # # Input = above array of length 17 # Fully connected layer on all 4 rows with 5 outputs: # [ 1.71536088 -0.72340977 -1.22485089 -2.5412786 -0.16338299]