LSTM

蔚落 2022-01-12 01:37 270阅读 0赞

# MINIST的循环神经网络--LSTM #

首先加载数据  
然后构建模型

> 首先设置训练的超参数，分别设置学习率，训练次数和每轮训练的数据大小  
> 定义输入数据及权重  
> 定义模型  
> 训练和评估模型

#Inspired by https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py
    import tensorflow as tf
    from tensorflow.contrib import rnn
    
    import numpy as np
    from tensorflow.examples.tutorials.mnist import input_data
    
    # configuration
    #                        O * W + b -> 10 labels for each image, O[? 28], W[28 10], B[10]
    #                       ^ (O: output 28 vec from 28 vec input)
    #                       |
    #      +-+  +-+       +--+
    #      |1|->|2|-> ... |28| time_step_size = 28
    #      +-+  +-+       +--+
    #       ^    ^    ...  ^
    #       |    |         |
    # img1:[28] [28]  ... [28]
    # img2:[28] [28]  ... [28]
    # img3:[28] [28]  ... [28]
    # ...
    # img128 or img256 (batch_size or test_size 256)
    #      each input size = input_vec_size=lstm_size=28
    
    # configuration variables
    input_vec_size = lstm_size = 28
    time_step_size = 28
    
    batch_size = 128
    test_size = 256
    
    def init_weights(shape):
        return tf.Variable(tf.random_normal(shape, stddev=0.01))
    
    
    def model(X, W, B, lstm_size):
        # X, input shape: (batch_size, time_step_size, input_vec_size)
        XT = tf.transpose(X, [1, 0, 2])  # permute time_step_size and batch_size
        # XT shape: (time_step_size, batch_size, input_vec_size)
        XR = tf.reshape(XT, [-1, lstm_size]) # each row has input for each lstm cell (lstm_size=input_vec_size)
        # XR shape: (time_step_size * batch_size, input_vec_size)
        X_split = tf.split(XR, time_step_size, 0) # split them to time_step_size (28 arrays)
        # Each array shape: (batch_size, input_vec_size)
    
        # Make lstm with lstm_size (each input vector size)
        lstm = rnn.BasicLSTMCell(lstm_size, forget_bias=1.0, state_is_tuple=True)
    
        # Get lstm cell output, time_step_size (28) arrays with lstm_size output: (batch_size, lstm_size)
        outputs, _states = rnn.static_rnn(lstm, X_split, dtype=tf.float32)
    
        # Linear activation
        # Get the last output
        return tf.matmul(outputs[-1], W) + B, lstm.state_size # State size to initialize the stat
    
    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)
    teX = teX.reshape(-1, 28, 28)
    
    X = tf.placeholder("float", [None, 28, 28])
    Y = tf.placeholder("float", [None, 10])
    
    # get lstm_size and output 10 labels
    W = init_weights([lstm_size, 10])
    B = init_weights([10])
    
    py_x, state_size = model(X, W, B, lstm_size)
    
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
    train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
    predict_op = tf.argmax(py_x, 1)
    
    session_conf = tf.ConfigProto()
    session_conf.gpu_options.allow_growth = True
    
    # Launch the graph in a session
    with tf.Session(config=session_conf) as sess:
        # you need to initialize all variables
        tf.global_variables_initializer().run()
    
        for i in range(100):
            for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX)+1, batch_size)):
                sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
    
            test_indices = np.arange(len(teX))  # Get A Test Batch
            np.random.shuffle(test_indices)
            test_indices = test_indices[0:test_size]
    
            print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
                             sess.run(predict_op, feed_dict={X: teX[test_indices]})))

> 0 0.69140625  
> 1 0.81640625  
> 2 0.88671875  
> 3 0.921875  
> 4 0.91015625  
> 5 0.953125  
> 6 0.9453125  
> 7 0.95703125  
> 8 0.96484375  
> 9 0.953125  
> 10 0.9765625  
> 11 0.9609375  
> 12 0.9609375  
> 13 0.93359375  
> 14 0.97265625  
> 15 0.984375  
> 16 0.98828125  
> 17 0.97265625  
> 18 0.9765625

转载于:https://www.cnblogs.com/Ann21/p/10484547.html