技術標籤：機器學習

Convolutional Neural Networks翻譯為卷積神經網路，常用在影象識別和語音分析等領域。CNN詳細介紹參看：

• https://en.wikipedia.org/wiki/Convolutional_neural_network
• http://blog.csdn.net/zouxy09/article/details/8781543
• http://deeplearning.net/tutorial/lenet.html

使用TensorFlow建立CNN

# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np

# 下載mnist資料集
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('./mnist_data/', one_hot=True)

# from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
#
# mnist = read_data_sets('./mnist_data/', one_hot=True)

n_output_layer = 10


# 定義待訓練的神經網路
def convolutional_neural_network(data):
    weights = {'w_conv1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
               'w_conv2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
               'w_fc': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),
               'out': tf.Variable(tf.random_normal([1024, n_output_layer]))}

    biases = {'b_conv1': tf.Variable(tf.random_normal([32])),
              'b_conv2': tf.Variable(tf.random_normal([64])),
              'b_fc': tf.Variable(tf.random_normal([1024])),
              'out': tf.Variable(tf.random_normal([n_output_layer]))}

    data = tf.reshape(data, [-1, 28, 28, 1])

    conv1 = tf.nn.relu(
        tf.add(tf.nn.conv2d(data, weights['w_conv1'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv1']))
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

    conv2 = tf.nn.relu(
        tf.add(tf.nn.conv2d(conv1, weights['w_conv2'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv2']))
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

    fc = tf.reshape(conv2, [-1, 7 * 7 * 64])
    fc = tf.nn.relu(tf.add(tf.matmul(fc, weights['w_fc']), biases['b_fc']))

    # dropout剔除一些"神經元"
    # fc = tf.nn.dropout(fc, 0.8)

    output = tf.add(tf.matmul(fc, weights['out']), biases['out'])
    return output


# 每次使用100條資料進行訓練
batch_size = 100

X = tf.placeholder('float', [None, 28 * 28])
Y = tf.placeholder('float')


# 使用資料訓練神經網路
def train_neural_network(X, Y):
    predict = convolutional_neural_network(X)
    # cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict,labels=Y))
    cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=predict, labels=Y))
    optimizer = tf.train.AdamOptimizer().minimize(cost_func)  # learning rate 預設 0.001

    epochs = 1
    with tf.Session() as session:
        # session.run(tf.initialize_all_variables())
        session.run(tf.global_variables_initializer())
        epoch_loss = 0
        for epoch in range(epochs):
            for i in range(int(mnist.train.num_examples / batch_size)):
                x, y = mnist.train.next_batch(batch_size)
                _, c = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})
                epoch_loss += c
            print(epoch, ' : ', epoch_loss)

        correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('準確率: ', accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))


train_neural_network(X, Y)

 

執行結果：

準確率:0.9789

tflearn

下面使用tflearn重寫上面程式碼，tflearn是TensorFlow的高階封裝，類似Keras。

tflearn提供了更簡單、直觀的介面。和scikit-learn差不多，程式碼如下：

# -*- coding:utf-8 -*-
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression


train_x, train_y, test_x, test_y = tflearn.datasets.mnist.load_data(
    data_dir="./mnist_data/",one_hot=True)

train_x = train_x.reshape(-1, 28, 28, 1)
test_x = test_x.reshape(-1, 28, 28, 1)

# 定義神經網路模型
conv_net = input_data(shape=[None, 28, 28, 1], name='input')
conv_net = conv_2d(conv_net, 32, 2, activation='relu')
conv_net = max_pool_2d(conv_net, 2)
conv_net = conv_2d(conv_net, 64, 2, activation='relu')
conv_net = max_pool_2d(conv_net, 2)
conv_net = fully_connected(conv_net, 1024, activation='relu')
conv_net = dropout(conv_net, 0.8)
conv_net = fully_connected(conv_net, 10, activation='softmax')
conv_net = regression(conv_net, optimizer='adam', loss='categorical_crossentropy', name='output')

model = tflearn.DNN(conv_net)

# 訓練
model.fit({'input': train_x}, {'output': train_y}, n_epoch=13,
          validation_set=({'input': test_x}, {'output': test_y}),
          snapshot_step=300, show_metric=True, run_id='mnist')

model.save('./mnist.model')  # 儲存模型

"""
model.load('mnist.model')   # 載入模型
model.predict([test_x[1]])  # 預測
"""

 

來源：斗大的熊貓