猫狗数据集

2021-11-24 02:10 374阅读 0赞

import numpy as np import pickle import cv2 import pandas as pd import tensorflow as tf import matplotlib.pyplot as plt #mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) train_data = { b'data': [], b'labels': []} with open("D:/TensorFlow_gpu/animal.pickle", mode='rb') as file: data = pickle.load(file, encoding='bytes') train_data[b'data'] += list(data['train_images']) train_data[b'labels'] += list(data['train_label']) train_epochs = 802 # 训练轮数 batch_size = 40 # 随机出去数据大小 display_step = 10 # 显示训练结果的间隔 learning_rate = 0.000001 # 学习效率 drop_prob = 0.2 # 正则化,丢弃比例 fch_nodes = 256 # 全连接隐藏层神经元的个数 def weight_init(shape): weights = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float32)#符合正太分布mean=0 #weights = tf.truncated_normal(shape, mean=0.01, stddev=0.1, dtype=tf.float32) return tf.Variable(weights) # 偏置的初始化 def biases_init(shape): biases = tf.random_normal(shape, dtype=tf.float32) # biases = tf.random_normal(shape, mean=-0.01, stddev=0.1, dtype=tf.float32) return tf.Variable(biases) # 随机选取mini_batch def get_random_batchdata(n_samples, batchsize): start_index = np.random.randint(0, n_samples - batchsize) return (start_index, start_index + batchsize) def xavier_init(layer1, layer2, constant=1): Min = -constant * np.sqrt(6.0 / (layer1 + layer2)) Max = constant * np.sqrt(6.0 / (layer1 + layer2)) return tf.Variable(tf.random_uniform((layer1, layer2), minval=Min, maxval=Max, dtype=tf.float32)) def conv2d(x, w): return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') x = tf.placeholder(tf.float32, [None, 224,224,3]) y = tf.placeholder(tf.float32, [None, 2]) # 把灰度图像一维向量,转换为28x28二维结构 x_image = x w_conv1 = weight_init([3, 3, 3, 96]) # 3*3,深度为3,96 b_conv1 = biases_init([96]) h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) # 输出张量的尺寸:112 h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_init([3, 3, 96, 96]) b_conv2 = biases_init([96]) h_conv2 = tf.nn.tanh(conv2d(h_pool1, W_conv2) + b_conv2)#输出是56 h_pool2 = max_pool_2x2(h_conv2)#池化后输出16*16*96 #2-1 W_conv3 = weight_init([3, 3, 96, 128]) b_conv3 = biases_init([128]) h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)#输出28 h_pool3 = max_pool_2x2(h_conv3)#池化后输出16*16*96 #第2层卷积2-2 W_conv4 = weight_init([3, 3, 128, 128]) b_conv4 = biases_init([128]) h_conv4 = tf.nn.tanh(conv2d(h_pool3, W_conv4) + b_conv4)#14 h_pool4 = max_pool_2x2(h_conv4)#池化输出8*8*128 #3-1 W_conv5 = weight_init([3, 3, 128, 256]) b_conv5 = biases_init([256]) h_conv5 = tf.nn.relu(conv2d(h_pool4, W_conv5) + b_conv5)#7*7*256 h_pool5 = max_pool_2x2(h_conv5)# h_pool5_flat = tf.reshape(h_pool5, [-1, 7 * 7 * 256]) w_fc1 = xavier_init(7 * 7 * 256, fch_nodes) b_fc1 = biases_init([fch_nodes]) h_fc1 = tf.nn.relu(tf.matmul(h_pool5_flat, w_fc1) + b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=drop_prob) # 隐藏层与输出层权重初始化 w_fc2 = xavier_init(fch_nodes, 2) b_fc2 = biases_init([2]) # 未激活的输出 y_ = tf.add(tf.matmul(h_fc1, w_fc2), b_fc2) #y_ = tf.add(tf.matmul(h_fc1_drop, w_fc2), b_fc2) # 激活后的输出 y_out = tf.nn.softmax(y_) #y_out = tf.nn.sigmoid(y_) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_out), reduction_indices=[1])) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy) #optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy) # 准确率 # 每个样本的预测结果是一个(1,10)的vector correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_out, 1)) # tf.cast把bool值转换为浮点数 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) init = tf.global_variables_initializer() #mnist = input_data.read_data_sets('MNIST/mnist', one_hot=True) n_samples = int(1800) total_batches = int(n_samples / batch_size) #x_train = np.array(train_data[b'data']) / 255 x_train = np.array(train_data[b'data']) y_train = np.array(pd.get_dummies(train_data[b'labels'])) #x_test = test_data[b'data'] / 255

转载于:https://www.cnblogs.com/TheKat/p/11115554.html