上一篇搭建了一個簡單的cnn網絡用來識別手寫數字。
基于tensorflow搭建一個簡單的CNN模型(code)
這次我們將要搭建一個較復雜的卷積神經網絡結構去對CIFAR-10進行訓練和識別。
1. load 一些必要的庫和 start a graph session:
import os
import sys
import tarfile
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from six.moves import urllib
sess = tf. Session()
2. 定義一些模型參數
batch_size = 128
output_every = 50
generations = 20000
eval_every = 500
image_height = 32
image_width = 32
crop_height = 24
crop_width = 24
num_channels = 3
num_targets = 10
data_dir = 'temp'
extract_folder = 'cifar-10-batches-bin'
3. 定義訓練學習率等幾個參數
learning_rate = 0.1
lr_decay = 0.9
num_gens_to_wait = 250
4. 現在我們建立可以讀取二進制 CIFAR-10圖片的參數
image_vec_length = image_height * image_width * num_channels
record_length = 1 + image_vec_length
5. 建立數據的路徑及下載CIFAR-10數據集圖片
data_dir = 'temp'
if not os.path.exists(data_dir):
? ? os.makedirs(data_dir)
? ? cifar10_url = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'
? ? data_file = os.path.join(data_dir, 'cifar-10-binary.tar.gz')
if not os.path.isfile(data_file):
? ? # Download file
? ? filepath, _ = urllib.request.urlretrieve(cifar10_url, data_file, progress)
? ? # Extract file
? ? tarfile.open(filepath, 'r:gz').extractall(data_dir)
6. 建立函數讀取隨機扭曲的圖片
def read_cifar_files(filename_queue, distort_images = True):
? ? reader = tf.FixedLengthRecordReader(record_bytes=record_length)
? ? key, record_string = reader.read(filename_queue)
? ? record_bytes = tf.decode_raw(record_string, tf.uint8)
? ? # Extract label
? ? image_label = tf.cast(tf.slice(record_bytes, [0], [1]),
? ? tf.int32)
? ? # Extract image
? ? image_extracted = tf.reshape(tf.slice(record_bytes, [1],
? ? [image_vec_length]), [num_channels, image_height, image_width])
? ? # Reshape image
? ? image_uint8image = tf.transpose(image_extracted, [1, 2, 0])
? ? reshaped_image = tf.cast(image_uint8image, tf.float32)
? ? # Randomly Crop image
? ? final_image = tf.image.resize_image_with_crop_or_pad(reshaped_
? ? image, crop_width, crop_height)
? ? if distort_images:
? ? ? ? # Randomly flip the image horizontally, change the brightness and contrast
? ? ? ? final_image = tf.image.random_flip_left_right(final_image)
? ? ? ? final_image = tf.image.random_brightness(final_image,max_delta=63)
? ? ? ? final_image = tf.image.random_contrast(final_
? ? ? ? image,lower=0.2, upper=1.8)
? ? # Normalize whitening
注意## For anyone else who has this problem, per_image_whitening was ? ? ? ? replaced by per_image_standardization
? ? # final_image = tf.image.per_image_whitening(final_image)
? ? final_image = tf.image.per_image_standardization(final_image)
? ? return(final_image, image_label)
## by per_image_standardization in v0.12
## For anyone else who has this problem, per_image_whitening was replaced
## by per_image_standardization in v0.12
final_image = tf.image.per_image_standardization(final_image)
7. 定義一個函數傳入數據
def input_pipeline(batch_size, train_logical=True):
? ? if train_logical:
? ? ? ? files = [os.path.join(data_dir, extract_folder, 'data_
? ? ? ? batch_{}.bin'.format(i)) for i in range(1,6)]
? ? else:
? ? ? ? files = [os.path.join(data_dir, extract_folder, 'test_batch.bin')]
? ? filename_queue = tf.train.string_input_producer(files)
? ? image, label = read_cifar_files(filename_queue)
? ? min_after_dequeue = 1000
? ? capacity = min_after_dequeue + 3 * batch_size
? ? example_batch, label_batch = tf.train.shuffle_batch([image,
? ? label], batch_size, capacity, min_after_dequeue)
? ? return(example_batch, label_batch)
8. 定義模型
# Define the model architecture, this will return logits from images
def cifar_cnn_model(input_images, batch_size, train_logical=True):
? ? def truncated_normal_var(name, shape, dtype):
? ? ? ? ?return(tf.get_variable(name=name, shape=shape, dtype=dtype, ? ? ? ? initializer=tf.truncated_normal_initializer(stddev=0.05)))
? ? def zero_var(name, shape, dtype):
? ? ? ? ?return(tf.get_variable(name=name, shape=shape, dtype=dtype, initializer=tf.constant_initializer(0.0)))
? ? ? ? ?# First Convolutional Layer
? ? with tf.variable_scope('conv1') as scope:
? ? ? ? ? # Conv_kernel is 5x5 for all 3 colors and we will create 64 features
? ? ? ? ?conv1_kernel = truncated_normal_var(name='conv_kernel1', shape=[5, 5, 3, 64], dtype=tf.float32)
? ? ? ? ?# We convolve across the image with a stride size of 1
? ? ? ? ?conv1 = tf.nn.conv2d(input_images, conv1_kernel, [1, 1, 1, 1], padding='SAME')
? ? ? ? ?# Initialize and add the bias term
? ? ? ? ?conv1_bias = zero_var(name='conv_bias1', shape=[64], dtype=tf.float32)
? ? ? ? ?conv1_add_bias = tf.nn.bias_add(conv1, conv1_bias)
? ? ? ? ?# ReLU element wise
? ? ? ? ?relu_conv1 = tf.nn.relu(conv1_add_bias)
? ? ? ? ?# Max Pooling
? ? ? ? ?pool1 = tf.nn.max_pool(relu_conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],padding='SAME', name='pool_layer1')
? ? ? ? ?# Local Response Normalization (parameters from paper)
? ? ? ? ?# paper: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
? ? ? ? ?norm1 = tf.nn.lrn(pool1, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm1')
? ? ? ? ?# Second Convolutional Layer
? ? with tf.variable_scope('conv2') as scope:
? ? ? ? ?# Conv kernel is 5x5, across all prior 64 features and we create 64 more features
? ? ? ? ?conv2_kernel = truncated_normal_var(name='conv_kernel2', shape=[5, 5, 64, 64], dtype=tf.float32)
? ? ? ? ?# Convolve filter across prior output with stride size of 1
? ? ? ? ?conv2 = tf.nn.conv2d(norm1, conv2_kernel, [1, 1, 1, 1], padding='SAME')
? ? ? ? ?# Initialize and add the bias
? ? ? ? ?conv2_bias = zero_var(name='conv_bias2', shape=[64], dtype=tf.float32)
? ? ? ? ?conv2_add_bias = tf.nn.bias_add(conv2, conv2_bias)
? ? ? ? ?# ReLU element wise
? ? ? ? ?relu_conv2 = tf.nn.relu(conv2_add_bias)
? ? ? ? ?# Max Pooling
? ? ? ? ?pool2 = tf.nn.max_pool(relu_conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], ? ? ? ? ? padding='SAME', name='pool_layer2')
? ? ? ? ?# Local Response Normalization (parameters from paper)
? ? ? ? ?norm2 = tf.nn.lrn(pool2, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm2')
? ? ? ? ?# Reshape output into a single matrix for multiplication for the fully connected layers
? ? ? ? ?reshaped_output = tf.reshape(norm2, [batch_size, -1])
? ? ? ? ?reshaped_dim = reshaped_output.get_shape()[1].value
? ? ? ? ?# First Fully Connected Layer
? ? with tf.variable_scope('full1') as scope:
? ? ? ? # Fully connected layer will have 384 outputs.
? ? ? ? full_weight1 = truncated_normal_var(name='full_mult1', shape=[reshaped_dim, 384], dtype=tf.float32)
? ? ? ? full_bias1 = zero_var(name='full_bias1', shape=[384], dtype=tf.float32)
? ? ? ? full_layer1 = tf.nn.relu(tf.add(tf.matmul(reshaped_output, full_weight1), full_bias1))
? ? ? ? # Second Fully Connected Layer
? ? with tf.variable_scope('full2') as scope:
? ? ? ? # Second fully connected layer has 192 outputs.
? ? ? ? full_weight2 = truncated_normal_var(name='full_mult2', shape=[384, 192], dtype=tf.float32)
? ? ? ? full_bias2 = zero_var(name='full_bias2', shape=[192], dtype=tf.float32)
? ? ? ? full_layer2 = tf.nn.relu(tf.add(tf.matmul(full_layer1, full_weight2), full_bias2))
? ? ? ? # Final Fully Connected Layer -> 10 categories for output (num_targets)
? ? with tf.variable_scope('full3') as scope:
? ? ? ? # Final fully connected layer has 10 (num_targets) outputs.
? ? ? ? full_weight3 = truncated_normal_var(name='full_mult3', shape=[192, ? ? ? num_targets], dtype=tf.float32)
? ? ? ? full_bias3 =? zero_var(name='full_bias3', shape=[num_targets], dtype=tf.float32)
? ? ? ? final_output = tf.add(tf.matmul(full_layer2, full_weight3), full_bias3)
? ? ? ? return(final_output)
9. ?定義loss函數
def cifar_loss(logits, targets):
? ? # Get rid of extra dimensions and cast targets into integers
? ? targets = tf.squeeze(tf.cast(targets, tf.int32))
? ? # Calculate cross entropy from logits and targets
? ? cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets)
? ? # Take the average loss across batch size
? ? cross_entropy_mean = tf.reduce_mean(cross_entropy)
? ? return(cross_entropy_mean)
10.定義訓練，其中學習率將要以指數下降。
def train_step(loss_value, generation_num):
? ? # Our learning rate is an exponential decay (stepped down)
? ? model_learning_rate = tf.train.exponential_decay(learning_rate, generation_num, num_gens_to_wait, lr_decay, staircase=True)
? ? # Create optimizer
? ? my_optimizer = tf.train.GradientDescentOptimizer(model_learning_rate)
? ? # Initialize train step
? ? train_step = my_optimizer.minimize(loss_value)
? ? return(train_step)
11. 計算準確率
def accuracy_of_batch(logits, targets):
? ? # Make sure targets are integers and drop extra dimensions
? ? targets = tf.squeeze(tf.cast(targets, tf.int32))
? ? # Get predicted values by finding which logit is the greatest
? ? batch_predictions = tf.cast(tf.argmax(logits, 1), tf.int32)
? ? # Check if they are equal across the batch
? ? predicted_correctly = tf.equal(batch_predictions, targets)
? ? # Average the 1's and 0's (True's and False's) across the batch size
? ? accuracy = tf.reduce_mean(tf.cast(predicted_correctly, tf.float32))
? ? return(accuracy)
12.輸入圖片
images, targets = input_pipeline(batch_size, train_logical=True)
test_images, test_targets = input_pipeline(batch_size, train_logical=False)
13. 聲明訓練模型和測試時模型用同樣的變量
with tf.variable_scope('model_definition') as scope:
? ? # Declare the training network model
? ? model_output = cifar_cnn_model(images, batch_size)
? ? # Use same variables within scope
? ? scope.reuse_variables()
? ? # Declare test model output
? ? test_output = cifar_cnn_model(test_images, batch_size)
14.初始化loss和測試精度函數
loss = cifar_loss(model_output, targets)
accuracy = accuracy_of_batch(test_output, test_targets)
generation_num = tf.Variable(0, trainable=False)
train_op = train_step(loss, generation_num)
15. 初始化網絡的所有變量
# Initialize Variables
print('Initializing the Variables.')
init = tf.initialize_all_variables()
sess.run(init)
# Initialize queue (This queue will feed into the model, so no placeholders necessary)
tf.train.start_queue_runners(sess=sess)
16. 迭代訓練，保存loss和測試accuracy
# Train CIFAR Model
print('Starting Training')
train_loss = []
test_accuracy = []
for i in range(generations):
? ? _, loss_value = sess.run([train_op, loss])
? ? if (i+1) % output_every == 0:
? ? ? ? train_loss.append(loss_value)
? ? ? ? output = 'Generation {}: Loss = {:.5f}'.format((i+1), loss_value)
? ? ? ? print(output)
? ? if (i+1) % eval_every == 0:
? ? ? ? [temp_accuracy] = sess.run([accuracy])
? ? ? ? test_accuracy.append(temp_accuracy)
? ? ? ? acc_output = ' --- Test Accuracy = {:.2f}%.'.format(100.*temp_accuracy)
? ? ? ? print(acc_output)
17.使用 matplotlib 講loss和測試accuracy圖像輸出來
# Print loss and accuracy
# Matlotlib code to plot the loss and accuracies
eval_indices = range(0, generations, eval_every)
output_indices = range(0, generations, output_every)
# Plot loss over time
plt.plot(output_indices, train_loss, 'k-')
plt.title('Softmax Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Softmax Loss')
plt.show()
# Plot accuracy over time
plt.plot(eval_indices, test_accuracy, 'k-')
plt.title('Test Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.show()
