tensorflow_二分类之tensorboard可视化
import tensorflow as tf
import time
from datetime import timedelta
import math
import random
import numpy as np
import os
#Adding Seed so that random initialization is consistent
from numpy.random import seed
seed(1)
from tensorflow import set_random_seed
set_random_seed(2)
batch_size = 32
#Prepare input data
classes = os.listdir('training_data')
num_classes = len(classes)
# 20% of the data will automatically be used for validation
validation_size = 0.2
img_size =128 #=crop_size
num_channels = 3
train_path='training_data'
# We shall load all the training and validation images and labels into memory using openCV and use that during training
data = dataset.read_train_sets(train_path, img_size, classes, validation_size=validation_size)
print("Complete reading input data. Will Now print a snippet of it")
print("Number of files in Training-set:\t\t{}".format(len(data.train.labels)))
print("Number of files in Validation-set:\t{}".format(len(data.valid.labels)))
session = tf.Session()
x = tf.placeholder(tf.float32, shape=[None, img_size,img_size,num_channels],name='x')
## labels
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, dimension=1)
##Network graph params
filter_size_conv1 = 3
num_filters_conv1 = 32
filter_size_conv2 = 3
num_filters_conv2 = 32
filter_size_conv3 = 3
num_filters_conv3 = 64
fc_layer_size = 128
tf.merged=None#tensorboard
def create_weights(shape):
return tf.Variable(tf.truncated_normal(shape, stddev=0.05)) #weight_decay
def create_biases(size):
return tf.Variable(tf.constant(0.05, shape=[size]))
def create_convolutional_layer(input,
num_input_channels,
conv_filter_size,
num_filters):
with tf.name_scope('input'):
## We shall define the weights that will be trained using create_weights function.
weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters])
tf.summary.histogram('weights',weights)#tensorboard
## We create biases using the create_biases function. These are also trained.
biases = create_biases(num_filters)
tf.summary.histogram('biases',biases)#tensorboard
## Creating the convolutional layer
layer = tf.nn.conv2d(input=input,
filter=weights,
strides=[1, 1, 1, 1],
padding='SAME')
layer += biases
## We shall be using max-pooling.
layer = tf.nn.max_pool(value=layer,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
## Output of pooling is fed to Relu which is the activation function for us.
layer = tf.nn.relu(layer)
return layer
def create_flatten_layer(layer):
#We know that the shape of the layer will be [batch_size img_size img_size num_channels]
# But let's get it from the previous layer.
layer_shape = layer.get_shape()
## Number of features will be img_height * img_width* num_channels. But we shall calculate it in place of hard-coding it.
num_features = layer_shape[1:4].num_elements()
## Now, we Flatten the layer so we shall have to reshape to num_features
layer = tf.reshape(layer, [-1, num_features])
return layer
def create_fc_layer(input,
num_inputs,
num_outputs,
use_relu='True'):
with tf.name_scope('fc'):#tensorboard
#Let's define trainable weights and biases.
weights = create_weights(shape=[num_inputs, num_outputs])
biases = create_biases(num_outputs)
tf.summary.histogram('weights',weights)#tensorboard
tf.summary.histogram('biases',biases)#tensorboard
# Fully connected layer takes input x and produces wx+b.Since, these are matrices, we use matmul function in Tensorflow
layer = tf.matmul(input, weights) + biases
if use_relu:
layer = tf.nn.relu(layer)
return layer
layer_conv1 = create_convolutional_layer(input=x,
num_input_channels=num_channels,
conv_filter_size=filter_size_conv1,
num_filters=num_filters_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
num_input_channels=num_filters_conv1,
conv_filter_size=filter_size_conv2,
num_filters=num_filters_conv2)
layer_conv3= create_convolutional_layer(input=layer_conv2,
num_input_channels=num_filters_conv2,
conv_filter_size=filter_size_conv3,
num_filters=num_filters_conv3)
layer_flat = create_flatten_layer(layer_conv3)
layer_fc1 = create_fc_layer(input=layer_flat,
num_inputs=layer_flat.get_shape()[1:4].num_elements(),
num_outputs=fc_layer_size,
use_relu=True)
layer_fc2 = create_fc_layer(input=layer_fc1,
num_inputs=fc_layer_size,
num_outputs=num_classes,
use_relu=False)
y_pred = tf.nn.softmax(layer_fc2,name='y_pred')
y_pred_cls = tf.argmax(y_pred, dimension=1)
session.run(tf.global_variables_initializer())
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,labels=y_true)
cost = tf.reduce_mean(cross_entropy)
tf.summary.scalar('cost',cost)#tensorboard
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)#base_lr
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy',accuracy)
tf.merged = tf.summary.merge_all()#tensorboard zaichushihuasuoyoubianliangzhiqian
session.run(tf.global_variables_initializer())
writer=tf.summary.FileWriter('tensorboard/test2', session.graph)#tensorboard rizhiwenjian
def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
with tf.name_scope('accuracy'):#tensorboard
acc = session.run(accuracy, feed_dict=feed_dict_train)
val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Validation Loss: {3:.3f}"
print(msg.format(epoch + 1, acc, val_acc, val_loss))
total_iterations = 0
saver = tf.train.Saver()
def train(num_iteration):
global total_iterations
for i in range(total_iterations,
total_iterations + num_iteration):
x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size)
feed_dict_tr = {x: x_batch,
y_true: y_true_batch}
feed_dict_val = {x: x_valid_batch,
y_true: y_valid_batch}
_, summary=session.run([optimizer,tf.merged ],feed_dict=feed_dict_tr)
writer.add_summary(summary, i)
if i % int(data.train.num_examples/batch_size) == 0:
val_loss = session.run(cost, feed_dict=feed_dict_val)
epoch = int(i / int(data.train.num_examples/batch_size))
show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)
saver.save(session, 'dogs-cats-model')
total_iterations += num_iteration
writer.close()#tensorboard
train(num_iteration=3000)
2）解析
构建log日志文件，在session之后的合适位置writer=tf.summary.FileWriter('tensorboard/test2', session.graph);
运行代码之后，会在tensorboard/test2文件下生成一个日志文件，然后在命令行中输入tensorboard --logdir tensorboard/test2
点开命令行输出的网址，即可打开以下页面：
可以看到graph图。
2）可以精简图中节点的名称，可以把一个模块定一个总的名字。
具体做法是在模块的上面使用tf.name_scope('名字'):
如：def create_convolutional_layer(input,
num_input_channels,
conv_filter_size,
num_filters):
with tf.name_scope('input'):
## We shall define the weights that will be trained using create_weights function.
weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters])
tf.summary.histogram('weights',weights)#tensorboard
## We create biases using the create_biases function. These are also trained.
biases = create_biases(num_filters)
tf.summary.histogram('biases',biases)#tensorboard
## Creating the convolutional layer
layer = tf.nn.conv2d(input=input,
filter=weights,
strides=[1, 1, 1, 1],
padding='SAME')
layer += biases
## We shall be using max-pooling.
layer = tf.nn.max_pool(value=layer,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
## Output of pooling is fed to Relu which is the activation function for us.
layer = tf.nn.relu(layer)
return layer
def create_flatten_layer(layer):
#We know that the shape of the layer will be [batch_size img_size img_size num_channels]
# But let's get it from the previous layer.
layer_shape = layer.get_shape()
## Number of features will be img_height * img_width* num_channels. But we shall calculate it in place of hard-coding it.
num_features = layer_shape[1:4].num_elements()
## Now, we Flatten the layer so we shall have to reshape to num_features
layer = tf.reshape(layer, [-1, num_features])
return layer
def create_fc_layer(input,
num_inputs,
num_outputs,
use_relu='True'):
with tf.name_scope('fc'):#tensorboard
#Let's define trainable weights and biases.
weights = create_weights(shape=[num_inputs, num_outputs])
biases = create_biases(num_outputs)
tf.summary.histogram('weights',weights)#tensorboard
tf.summary.histogram('biases',biases)#tensorboard
# Fully connected layer takes input x and produces wx+b.Since, these are matrices, we use matmul function in Tensorflow
layer = tf.matmul(input, weights) + biases
if use_relu:
layer = tf.nn.relu(layer)
return layer
layer_conv1 = create_convolutional_layer(input=x,
num_input_channels=num_channels,
conv_filter_size=filter_size_conv1,
num_filters=num_filters_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
num_input_channels=num_filters_conv1,
conv_filter_size=filter_size_conv2,
num_filters=num_filters_conv2)
layer_conv3= create_convolutional_layer(input=layer_conv2,
num_input_channels=num_filters_conv2,
conv_filter_size=filter_size_conv3,
num_filters=num_filters_conv3)
layer_flat = create_flatten_layer(layer_conv3)
layer_fc1 = create_fc_layer(input=layer_flat,
num_inputs=layer_flat.get_shape()[1:4].num_elements(),
num_outputs=fc_layer_size,
use_relu=True)
layer_fc2 = create_fc_layer(input=layer_fc1,
num_inputs=fc_layer_size,
num_outputs=num_classes,
use_relu=False)
y_pred = tf.nn.softmax(layer_fc2,name='y_pred')
y_pred_cls = tf.argmax(y_pred, dimension=1)
session.run(tf.global_variables_initializer())
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,labels=y_true)
cost = tf.reduce_mean(cross_entropy)
tf.summary.scalar('cost',cost)#tensorboard
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)#base_lr
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy',accuracy)
tf.merged = tf.summary.merge_all()#tensorboard zaichushihuasuoyoubianliangzhiqian
session.run(tf.global_variables_initializer())
writer=tf.summary.FileWriter('tensorboard/test2', session.graph)#tensorboard rizhiwenjian
def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
with tf.name_scope('accuracy'):#tensorboard
acc = session.run(accuracy, feed_dict=feed_dict_train)
val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Validation Loss: {3:.3f}"
print(msg.format(epoch + 1, acc, val_acc, val_loss))
total_iterations = 0
saver = tf.train.Saver()
def train(num_iteration):
global total_iterations
for i in range(total_iterations,
total_iterations + num_iteration):
x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size)
feed_dict_tr = {x: x_batch,
y_true: y_true_batch}
feed_dict_val = {x: x_valid_batch,
y_true: y_valid_batch}
_, summary=session.run([optimizer,tf.merged ],feed_dict=feed_dict_tr)
writer.add_summary(summary, i)
if i % int(data.train.num_examples/batch_size) == 0:
val_loss = session.run(cost, feed_dict=feed_dict_val)
epoch = int(i / int(data.train.num_examples/batch_size))
show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)
saver.save(session, 'dogs-cats-model')
total_iterations += num_iteration
writer.close()#tensorboard
train(num_iteration=3000)
2）解析
构建log日志文件，在session之后的合适位置writer=tf.summary.FileWriter('tensorboard/test2', session.graph);
运行代码之后，会在tensorboard/test2文件下生成一个日志文件，然后在命令行中输入tensorboard --logdir tensorboard/test2
点开命令行输出的网址，即可打开以下页面：
可以看到graph图。
2）可以精简图中节点的名称，可以把一个模块定一个总的名字。
具体做法是在模块的上面使用tf.name_scope('名字'):
如：def create_fc_layer(input,num_inputs,
num_outputs,
use_relu='True'):
with tf.name_scope('fc'):#tensorboard
#Let's define trainable weights and biases.
weights = create_weights(shape=[num_inputs, num_outputs])
biases = create_biases(num_outputs)
tf.summary.histogram('weights',weights)#tensorboard
tf.summary.histogram('biases',biases)#tensorboard
重新训练之后在打开tensorboard会发现图简洁了很多。
3）观察weight,biases,cost,accuracy变量等参数的变化
对于直方图输入以下内容：weights = create_weights(shape=[num_inputs, num_outputs])
biases = create_biases(num_outputs)
tf.summary.histogram('weights',weights)#tensorboard
tf.summary.histogram('biases',biases)#tensorboard
对于变量输入以下内容：cost = tf.reduce_mean(cross_entropy)
tf.summary.scalar('cost',cost)#tensorboard
在对以上需要可视化的参数的操作之后，session.run(tf.global_variables_initializer())之前，整合以上参数：
tf.merged = tf.summary.merge_all()#tensorboard zaichushihuasuoyoubianliangzhiqian
session.run(tf.global_variables_initializer())
然后在执行训练的代码中将以上参数的变化也写入到writer文件中。具体见下段代码中红色字体，尤其注意蓝色字体容易出错。训练完成之后，关闭writer文件，writer.close()
def train(num_iteration):
global total_iterations
for i in range(total_iterations,
total_iterations + num_iteration):
x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size)
feed_dict_tr = {x: x_batch,
y_true: y_true_batch}
feed_dict_val = {x: x_valid_batch,
y_true: y_valid_batch}
_, summary=session.run([optimizer,tf.merged ],feed_dict=feed_dict_tr)
writer.add_summary(summary, i)