【机器学习】Tensorflow学习笔记
2016-02-14 00:06
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构建网络模型
基本的MLP网络结构
基本的感知机模型,没有加入b模型:
Y=W∗XY = W*X
[code]import tensorflow as tf import numpy as np import input_data # 初始化权重 w def init_weights(shape): return tf.Variable(tf.random_normal(shape, stddev=0.01)) # 定义网络模型,只是基本的mlp模型,堆叠两层的逻辑回归 def model(X, w_h, w_o): h = tf.nn.sigmoid(tf.matmul(X, w_h)) return tf.matmul(h, w_o) #这里没有用softmax # 加载数据 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels # 定义占位符 X = tf.placeholder("float", [None, 784]) Y = tf.placeholder("float", [None, 10]) # 初始化模型参数 w_h = init_weights([784, 625]) w_o = init_weights([625, 10]) # 定义模型 py_x = model(X, w_h, w_o) # 定义损失函数 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # 定义训练操作 train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizer # 定义测试操作 predict_op = tf.argmax(py_x, 1) # 定义并初始化会话 sess = tf.Session() init = tf.initialize_all_variables() sess.run(init) # 训练测试 for i in range(100): for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)): sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]}) print i, np.mean(np.argmax(teY, axis=1) == sess.run(predict_op, feed_dict={X: teX, Y: teY}))
构建多层网络
模型:多层(3层模型)
[code]import tensorflow as tf import numpy as np import input_data # 初始化权重 def init_weights(shape): return tf.Variable(tf.random_normal(shape, stddev=0.01)) # 定义模型,2层的隐藏层+ 3层的dropout def model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden): X = tf.nn.dropout(X, p_drop_input) # 输入就开始用dropout h = tf.nn.relu(tf.matmul(X, w_h)) h = tf.nn.dropout(h, p_drop_hidden) # dropout h2 = tf.nn.relu(tf.matmul(h, w_h2)) h2 = tf.nn.dropout(h2, p_drop_hidden) # dropout return tf.matmul(h2, w_o) # 加载数据 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels # 定义占位符+ 初始化变量 X = tf.placeholder("float", [None, 784]) Y = tf.placeholder("float", [None, 10]) w_h = init_weights([784, 625]) w_h2 = init_weights([625, 625]) w_o = init_weights([625, 10]) # dropout 的概率 p_keep_input = tf.placeholder("float") p_keep_hidden = tf.placeholder("float") # 模型 py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden) # 损失函数 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) predict_op = tf.argmax(py_x, 1) sess = tf.Session() init = tf.initialize_all_variables() sess.run(init) for i in range(100): for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)): sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end], p_keep_input: 0.8, p_keep_hidden: 0.5}) print i, np.mean(np.argmax(teY, axis=1) == sess.run(predict_op, feed_dict={X: teX, Y: teY, p_keep_input: 1.0, p_keep_hidden: 1.0}))
卷积神经网络
模型:[code]import tensorflow as tf import numpy as np import input_data def init_weights(shape): return tf.Variable(tf.random_normal(shape, stddev=0.01)) # 定义卷积神经网络模型 def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden): l1a = tf.nn.relu(tf.nn.conv2d(X, w, [1, 1, 1, 1], 'SAME')) l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') l1 = tf.nn.dropout(l1, p_keep_conv) l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, [1, 1, 1, 1], 'SAME')) l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') l2 = tf.nn.dropout(l2, p_keep_conv) l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, [1, 1, 1, 1], 'SAME')) l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) l3 = tf.nn.dropout(l3, p_keep_conv) l4 = tf.nn.relu(tf.matmul(l3, w4)) l4 = tf.nn.dropout(l4, p_keep_hidden) pyx = tf.matmul(l4, w_o) return pyx # 加载数据 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels trX = trX.reshape(-1, 28, 28, 1) teX = teX.reshape(-1, 28, 28, 1) X = tf.placeholder("float", [None, 28, 28, 1]) Y = tf.placeholder("float", [None, 10]) w = init_weights([3, 3, 1, 32]) w2 = init_weights([3, 3, 32, 64]) w3 = init_weights([3, 3, 64, 128]) w4 = init_weights([128 * 4 * 4, 625]) w_o = init_weights([625, 10]) p_keep_conv = tf.placeholder("float") p_keep_hidden = tf.placeholder("float") py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden) # 损失函数 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # 训练操作 train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) # 测试操作 predict_op = tf.argmax(py_x, 1) sess = tf.Session() init = tf.initialize_all_variables() sess.run(init) for i in range(100): for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)): sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end], p_keep_conv: 0.8, p_keep_hidden: 0.5}) test_indices = np.arange(len(teX)) # Get A Test Batch np.random.shuffle(test_indices) test_indices = test_indices[0:256] print i, np.mean(np.argmax(teY[test_indices], axis=1) == sess.run(predict_op, feed_dict={X: teX[test_indices], Y: teY[test_indices], p_keep_conv: 1.0, p_keep_hidden: 1.0}))
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