How can I get every layers output value with keras? - neural-network

Question

I want use keras Lstm to get the time series features, then use the features to Kmeans. But now I can not get the layers output values. How can I get the layers output values?
This is my lstm network
Layer (type) Output Shape Param #
lstm_66 (LSTM) (None, None, 50) 10400
lstm_67 (LSTM) (None, 100) 60400
dense_19 (Dense) (None, 1) 101
activation_19 (Activation) (None, 1) 0
I want to get the lstm_67 output values,my code is:
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)
and the result is:
Tensor("lstm_61/TensorArrayReadV3:0", shape=(?, 100), dtype=float32)
So, how can I get the layers output value?
Thanks!
But it not work,my code is:
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

In TensorFlow 2.x, you can do like this:
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() should do.
Look at the documentation here and here

Answer 3

First of all, this is a tensor, you need to use the tf. Print () method to see the specific value. If you use Spyder, you will not see this information in the console. You need to execute this program in the command line.