如何使用 keras 获得每一层的输出值？

Question

我想使用 keras Lstm 获取时间序列特征，然后将这些特征用于 Kmeans。但现在我无法获得图层输出值。如何获取图层输出值？

这是我的 lstm 网络

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层（类型）输出形状参数#

lstm_66 (LSTM)（无，无，50）10400

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lstm_67 (LSTM) (无, 100) 60400

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dense_19（密集）（无，1）101

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activation_19（激活）（无，1）0

我想得到 lstm_67 的输出值，我的代码是：

import keras.backend as K
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 
import tensorflow as tf
sess = tf.Session()
sess.run(tf.global_variables_initializer())
import numpy as np
statesAll=[]
layers = model.layers
print layers[1].output,type(layers[1].output[1]),sess.run(layers[1].output)

结果是：

Tensor("lstm_61/TensorArrayReadV3:0", shape=(?, 100), dtype=float32)

那么，我怎样才能得到层的输出值呢？

谢谢！

但它不起作用，我的代码是：

def load_data(file_name, sequence_length=10, split=0.8):
    df = pd.read_csv(file_name, sep=',', usecols=[1])
    data_all = np.array(df).astype(float)
    scaler = MinMaxScaler()
    data_all = scaler.fit_transform(data_all)
    data = []
    print len(data_all)
    for i in range(len(data_all) - sequence_length - 1):
        data.append(data_all[i: i + sequence_length + 1])

    reshaped_data = np.array(data).astype('float64')
    np.random.shuffle(reshaped_data)
    x = reshaped_data[:, :-1]
    y = reshaped_data[:, -1]
    split_boundary = int(reshaped_data.shape[0] * split)
    train_x = x[: split_boundary]
    test_x = x[split_boundary:]

    train_y = y[: split_boundary]
    test_y = y[split_boundary:]

    return train_x, train_y, test_x, test_y, scaler

def build_model(n_samples, time_steps, input_dim):
    model = Sequential()
    model.add(LSTM(input_dim=1, output_dim=50,return_sequences=True))
    model.add(LSTM(100, return_sequences=False))
    model.add(Dense(output_dim=1))
    model.add(Activation('linear'))
    model.compile(loss='mse', optimizer='rmsprop')
    print(model.layers)
    return model

def train_model(train_x, train_y, test_x, test_y):
    model = build_model()
    model.fit(train_x, train_y, batch_size=128, nb_epoch=30,validation_split=0.1)
    return model


train_x, train_y, test_x, test_y, scaler = load_data(file path)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1))
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 1))

model = train_model(train_x, train_y, test_x, test_y)

from keras import backend as K
layers = model.layers
K.eval(layers[1].output)

Answer 1

在 TensorFlow 2.x 中，您可以这样做：

from tensorflow.python.keras import backend as K

model = build_model() 
# lstm_67 is the second layer.
lstm = K.function([model.layers[0].input], [model.layers[1].output])
lstm_output = lstm([test_x])[0]

Answer 2

keras.backend.eval() 应该可以。

查看文档here 和here

Answer 3

首先，这是一个张量，需要使用tf. Print ()方法查看具体值。如果您使用 Spyder，您将不会在控制台中看到此信息。你需要在命令行中执行这个程序。