《Tensorflow实战》之6.3VGGnet学习

# -*- coding: utf-8 -*-"""Created on Wed Dec 20 14:45:35 2017@author: Administrator"""#coding:utf-8# Copyright 2016 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## ????http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# ==============================================================================import tensorflow as tfimport numpy as npdata_name = ‘YaleB_32x32.mat‘sele_num ?= 10import matlab.engineeng = matlab.engine.start_matlab()t = eng.data_imread_MSE(data_name,sele_num)eng.quit()#t = np.array(t)Train_Ma ?= np.array(t[0]).astype(np.float32)Train_Lab = np.array(t[1]).astype(np.int8)Test_Ma ??= np.array(t[2]).astype(np.float32)Test_Lab ?= np.array(t[3]).astype(np.int8)Num_fea ??= Train_Ma.shape[1]Num_Class = Train_Lab.shape[1]image_row ???= 32image_column = 32def conv_op(input_op, name, kh, kw, n_out, dh, dw, p): ???n_in = input_op.get_shape()[-1].value ???with tf.name_scope(name) as scope: ???????kernel = tf.get_variable(scope+"w", ????????????????????????????????shape=[kh, kw, n_in, n_out], ????????????????????????????????dtype=tf.float32, ?????????????????????????????????initializer=tf.contrib.layers.xavier_initializer_conv2d()) ???????conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding=‘SAME‘) ???????bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32) ???????biases = tf.Variable(bias_init_val, trainable=True, name=‘b‘) ???????z = tf.nn.bias_add(conv, biases) ???????activation = tf.nn.relu(z, name=scope) ???????p += [kernel, biases] ???????return activation# 全连接层函数def fc_op(input_op, name, n_out, p): ???n_in = input_op.get_shape()[-1].value ???with tf.name_scope(name) as scope: ???????kernel = tf.get_variable(scope+"w", ????????????????????????????????shape=[n_in, n_out], ????????????????????????????????dtype=tf.float32, ?????????????????????????????????initializer=tf.contrib.layers.xavier_initializer()) ???????biases = tf.Variable(tf.constant(0.1, shape=[n_out], dtype=tf.float32), name=‘b‘) ???????activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope) ???????p += [kernel, biases] ???????return activationdef mpool_op(input_op, name, kh, kw, dh, dw): ???return tf.nn.max_pool(input_op, ?????????????????????????ksize=[1, kh, kw, 1], ?????????????????????????strides=[1, dh, dw, 1], ?????????????????????????padding=‘SAME‘, ?????????????????????????name=name) ???# assume input_op shape is 224x224x3sess = tf.InteractiveSession()# ---------- 定义 输入和输出 --------------- #x = tf.placeholder(tf.float32, [None, Num_fea])y_ = tf.placeholder(tf.float32, [None, Num_Class])x_image = tf.reshape(x, [-1,image_row,image_column,1])keep_prob = tf.placeholder(tf.float32) ???# block 1 -- outputs 112x112x64p = []conv1_1 = conv_op(x_image, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)conv1_2 = conv_op(conv1_1, ?name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)pool1 ??= mpool_op(conv1_2, ??name="pool1", ??kh=2, kw=2, dw=2, dh=2)# block 2 -- outputs 56x56x128conv2_1 = conv_op(pool1, ???name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)conv2_2 = conv_op(conv2_1, ?name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)pool2 ??= mpool_op(conv2_2, ??name="pool2", ??kh=2, kw=2, dh=2, dw=2)# # block 3 -- outputs 28x28x256conv3_1 = conv_op(pool2, ???name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)conv3_2 = conv_op(conv3_1, ?name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)conv3_3 = conv_op(conv3_2, ?name="conv3_3", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) ???pool3 ??= mpool_op(conv3_3, ??name="pool3", ??kh=2, kw=2, dh=2, dw=2)# block 4 -- outputs 14x14x512conv4_1 = conv_op(pool3, ???name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)conv4_2 = conv_op(conv4_1, ?name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)conv4_3 = conv_op(conv4_2, ?name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)pool4 ??= mpool_op(conv4_3, ??name="pool4", ??kh=2, kw=2, dh=2, dw=2)# block 5 -- outputs 7x7x512conv5_1 = conv_op(pool4, ???name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)conv5_2 = conv_op(conv5_1, ?name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)conv5_3 = conv_op(conv5_2, ?name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)pool5 ??= mpool_op(conv5_3, ??name="pool5", ??kh=2, kw=2, dw=2, dh=2)# flattenshp = pool5.get_shape()flattened_shape = shp[1].value * shp[2].value * shp[3].valueresh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1") ???# fully connectedfc6 = fc_op(resh1, name="fc6", n_out=4096, p=p)fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop")fc7 = fc_op(fc6_drop, name="fc7", n_out=4096, p=p)fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")fc8 = fc_op(fc7_drop, name="fc8", n_out=Num_Class, p=p)predictions = tf.nn.softmax(fc8)cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(predictions), reduction_indices=[1]))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(predictions,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))tf.global_variables_initializer().run()for i in range(1000): ???train_accuracy = accuracy.eval(feed_dict={ ???????x:Train_Ma, y_: Train_Lab, keep_prob: 1.0}) ???print("step %d, training accuracy %g"%(i, train_accuracy)) ???train_step.run(feed_dict={x: Train_Ma, y_: Train_Lab, keep_prob: 0.8})print("test accuracy %g"%accuracy.eval(feed_dict={ ???x: Test_Ma, y_: Test_Lab, keep_prob: 1.0}))

# -*- coding: utf-8 -*-"""Created on Wed Dec 20 15:40:44 2017@author: Administrator"""# -*- coding: utf-8 -*-"""Created on Wed Dec 20 14:45:35 2017@author: Administrator"""#coding:utf-8# Copyright 2016 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## ????http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# ==============================================================================import tensorflow as tfimport numpy as npdata_name = ‘YaleB_32x32.mat‘sele_num ?= 10import matlab.engineeng = matlab.engine.start_matlab()t = eng.data_imread_MSE(data_name,sele_num)eng.quit()#t = np.array(t)Train_Ma ?= np.array(t[0]).astype(np.float32)Train_Lab = np.array(t[1]).astype(np.int8)Test_Ma ??= np.array(t[2]).astype(np.float32)Test_Lab ?= np.array(t[3]).astype(np.int8)Num_fea ??= Train_Ma.shape[1]Num_Class = Train_Lab.shape[1]image_row ???= 32image_column = 32def conv_op(input_op, name, kh, kw, n_out, dh, dw, p): ???n_in = input_op.get_shape()[-1].value ???with tf.name_scope(name) as scope: ???????kernel = tf.get_variable(scope+"w", ????????????????????????????????shape=[kh, kw, n_in, n_out], ????????????????????????????????dtype=tf.float32, ?????????????????????????????????initializer=tf.contrib.layers.xavier_initializer_conv2d()) ???????conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding=‘SAME‘) ???????bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32) ???????biases = tf.Variable(bias_init_val, trainable=True, name=‘b‘) ???????z = tf.nn.bias_add(conv, biases) ???????activation = tf.nn.relu(z, name=scope) ???????p += [kernel, biases] ???????return activation# 全连接层函数def fc_op(input_op, name, n_out, p): ???n_in = input_op.get_shape()[-1].value ???with tf.name_scope(name) as scope: ???????kernel = tf.get_variable(scope+"w", ????????????????????????????????shape=[n_in, n_out], ????????????????????????????????dtype=tf.float32, ?????????????????????????????????initializer=tf.contrib.layers.xavier_initializer()) ???????biases = tf.Variable(tf.constant(0.1, shape=[n_out], dtype=tf.float32), name=‘b‘) ???????activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope) ???????p += [kernel, biases] ???????return activationdef mpool_op(input_op, name, kh, kw, dh, dw): ???return tf.nn.max_pool(input_op, ?????????????????????????ksize=[1, kh, kw, 1], ?????????????????????????strides=[1, dh, dw, 1], ?????????????????????????padding=‘SAME‘, ?????????????????????????name=name) ???# assume input_op shape is 224x224x3 ???# block 1 -- outputs 112x112x64def inference_op(input_op, keep_prob): ???p = [] ???# assume input_op shape is 224x224x3 ???# block 1 -- outputs 112x112x64 ???conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p) ???conv1_2 = conv_op(conv1_1, ?name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p) ???pool1 = mpool_op(conv1_2, ??name="pool1", ??kh=2, kw=2, dw=2, dh=2) ???# block 2 -- outputs 56x56x128 ???conv2_1 = conv_op(pool1, ???name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p) ???conv2_2 = conv_op(conv2_1, ?name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p) ???pool2 = mpool_op(conv2_2, ??name="pool2", ??kh=2, kw=2, dh=2, dw=2) ???# # block 3 -- outputs 28x28x256 ???conv3_1 = conv_op(pool2, ???name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) ???conv3_2 = conv_op(conv3_1, ?name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) ???conv3_3 = conv_op(conv3_2, ?name="conv3_3", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) ???????pool3 = mpool_op(conv3_3, ??name="pool3", ??kh=2, kw=2, dh=2, dw=2) ???# block 4 -- outputs 14x14x512 ???conv4_1 = conv_op(pool3, ???name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) ???conv4_2 = conv_op(conv4_1, ?name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) ???conv4_3 = conv_op(conv4_2, ?name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) ???pool4 = mpool_op(conv4_3, ??name="pool4", ??kh=2, kw=2, dh=2, dw=2) ???# block 5 -- outputs 7x7x512 ???conv5_1 = conv_op(pool4, ???name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) ???conv5_2 = conv_op(conv5_1, ?name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) ???conv5_3 = conv_op(conv5_2, ?name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) ???pool5 = mpool_op(conv5_3, ??name="pool5", ??kh=2, kw=2, dw=2, dh=2) ???# flatten ???shp = pool5.get_shape() ???flattened_shape = shp[1].value * shp[2].value * shp[3].value ???resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1") ???# fully connected ???fc6 = fc_op(resh1, name="fc6", n_out=4096, p=p) ???fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop") ???fc7 = fc_op(fc6_drop, name="fc7", n_out=4096, p=p) ???fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop") ???fc8 = fc_op(fc7_drop, name="fc8", n_out=Num_Class, p=p) ???predictions = tf.nn.softmax(fc8) ???return predictions, fc8, p# ---------- 定义 输入和输出 --------------- #sess = tf.InteractiveSession()x = tf.placeholder(tf.float32, [None, Num_fea])y_ = tf.placeholder(tf.float32, [None, Num_Class])x_image = tf.reshape(x, [-1,image_row,image_column,1])keep_prob = tf.placeholder(tf.float32)predictions, fc8, p = inference_op(x_image, keep_prob)cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(predictions), reduction_indices=[1]))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(predictions,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))tf.global_variables_initializer().run()for i in range(100): ???train_accuracy = accuracy.eval(feed_dict={ ???????x:Train_Ma, y_: Train_Lab, keep_prob: 1.0}) ???print("step %d, training accuracy %g"%(i, train_accuracy)) ???train_step.run(feed_dict={x: Train_Ma, y_: Train_Lab, keep_prob: 0.8})print("test accuracy %g"%accuracy.eval(feed_dict={ ???x: Test_Ma, y_: Test_Lab, keep_prob: 1.0}))