激活TensorFlow
activate tensorflow
安装Keras
pip install keras==2.1.2
测试
import keras
正确结果
Using TensorFlow backend.
其中2.1.2版本的Keras才能对应TensorFlow1.4
Spyder中测试代码
from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np
data_dim = 16
timesteps = 8
num_classes = 10
# expected input data shape: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32
model.add(LSTM(32)) # return a single vector of dimension 32
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# Generate dummy training data
x_train = np.random.random((1000, timesteps, data_dim))
y_train = np.random.random((1000, num_classes))
# Generate dummy validation data
x_val = np.random.random((100, timesteps, data_dim))
y_val = np.random.random((100, num_classes))
model.fit(x_train, y_train,
batch_size=64, epochs=1,
validation_data=(x_val, y_val))
测试正确结果:
Using TensorFlow backend.
Train on 1000 samples, validate on 100 samples
Epoch 1/1
1000/1000 [==============================] - 3s 3ms/step - loss: 11.6045 - acc: 0.0910 - val_loss: 11.7486 - val_acc: 0.1300