我想使用keras框架构建和训练神经网络。我配置keras它将使用Tensorflow作为后端。在我使用keras训练模型后,我尝试仅使用Tensorflow。我可以访问会话并获取张量流图。但我不知道如何使用张量流图来进行预测。
我使用以下教程构建网络 http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
在train()方法中我只使用keras构建和训练模型并保存keras和tensorflow模型
在eval()方法中
这是我的代码:
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
import keras.backend.tensorflow_backend as K
import tensorflow as tf
import numpy
sess = tf.Session()
K.set_session(sess)
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:, 0:8]
Y = dataset[:, 8]
def train():
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=10, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
# save tensorflow modell
saver = tf.train.Saver()
save_path = saver.save(sess, "model")
def eval():
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
score = loaded_model.evaluate(X, Y, verbose=0)
loaded_model.predict(X)
print ("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
# load tensorflow model
sess = tf.Session()
saver = tf.train.import_meta_graph('model.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
# TODO try to predict with the tensorflow model only
# without using keras functions
我可以访问keras框架为我构建的张量流图(sess.graph),但我不知道如何使用张量流图来预测。我知道如何构建张量流图并在generell中使用它进行预测,但不能使用模型keras为我构建。
答案 0 :(得分:3)
您需要从Keras模型定义中获取输入和输出张量,然后获取当前的TensorFlow会话。然后,您只能使用TensorFlow进行评估。假设model
是loaded_model
而x
是您的培训数据。
sess = K.get_session()
input_tensor = model.input
output_tensor = model.output
output_tensor.eval(feed_dict={input_tensor: x}, session=sess)