我有一个使用Keras和Tensorflow作为后端训练的模型，但是现在我需要将我的模型转换为特定应用程序的张量流图。我尝试执行此操作并进行了预测以确保其正常工作，但是与从model.predict（）收集的结果进行比较时，我得到了非常不同的值。例如：

from keras.models import load_model
import tensorflow as tf

model = load_model('model_file.h5')

x_placeholder = tf.placeholder(tf.float32, shape=(None,7214,1))
y = model(x_placeholder)

x = np.ones((1,7214,1))


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("Predictions from:\ntf graph:      "+str(sess.run(y, feed_dict={x_placeholder:x})))
    print("keras predict: "+str(model.predict(x)))

返回：

Predictions from:
tf graph:      [[-0.1015993   0.07432419  0.0592984 ]]
keras predict: [[ 0.39339241  0.57949686 -3.67846966]]

keras预测的值是正确的，但tf图的结果却不正确。

如果它有助于了解最终的预期应用程序，那么我将使用tf.gradients（）函数创建一个jacobian矩阵，但是与theano的jacobian函数进行比较（当前给出正确的jacobian函数）时，当前它无法返回正确的结果。这是我的tensorflow
jacobian代码：

x = tf.placeholder(tf.float32, shape=(None,7214,1))
y = tf.reshape(model(x)[0],[-1])
y_list = tf.unstack(y)

jacobian_list = [tf.gradients(y_, x)[0] for y_ in y_list]
jacobian = tf.stack(jacobian_list)

编辑：模型代码

import numpy as np

from keras.models import Sequential
from keras.layers import Dense, InputLayer, Flatten
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, ReduceLROnPlateau

# activation function used following every layer except for the output layers
activation = 'relu'

# model weight initializer
initializer = 'he_normal'

# shape of input data that is fed into the input layer
input_shape = (None,7214,1)

# number of filters used in the convolutional layers
num_filters = [4,16]

# length of the filters in the convolutional layers
filter_length = 8

# length of the maxpooling window 
pool_length = 4

# number of nodes in each of the hidden fully connected layers
num_hidden_nodes = [256,128]

# number of samples fed into model at once during training
batch_size = 64

# maximum number of interations for model training
max_epochs = 30

# initial learning rate for optimization algorithm
lr = 0.0007

# exponential decay rate for the 1st moment estimates for optimization algorithm
beta_1 = 0.9

# exponential decay rate for the 2nd moment estimates for optimization algorithm
beta_2 = 0.999

# a small constant for numerical stability for optimization algorithm
optimizer_epsilon = 1e-08

model = Sequential([

    InputLayer(batch_input_shape=input_shape),

    Conv1D(kernel_initializer=initializer, activation=activation, padding="same", filters=num_filters[0], kernel_size=filter_length),

    Conv1D(kernel_initializer=initializer, activation=activation, padding="same", filters=num_filters[1], kernel_size=filter_length),

    MaxPooling1D(pool_size=pool_length),

    Flatten(),

    Dense(units=num_hidden_nodes[0], kernel_initializer=initializer, activation=activation),

    Dense(units=num_hidden_nodes[1], kernel_initializer=initializer, activation=activation),

    Dense(units=3, activation="linear", input_dim=num_hidden_nodes[1]),
])

# compile model
loss_function = mean squared error
early_stopping_min_delta = 0.0001
early_stopping_patience = 4
reduce_lr_factor = 0.5
reuce_lr_epsilon = 0.0009
reduce_lr_patience = 2
reduce_lr_min = 0.00008

optimizer = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=optimizer_epsilon, decay=0.0)

early_stopping = EarlyStopping(monitor='val_loss',     min_delta=early_stopping_min_delta, 
                                   patience=early_stopping_patience, verbose=2, mode='min')

reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, epsilon=reuce_lr_epsilon, 
                              patience=reduce_lr_patience,     min_lr=reduce_lr_min, mode='min', verbose=2)

model.compile(optimizer=optimizer, loss=loss_function)

model.fit(train_x, train_y, validation_data=(cv_x, cv_y),
      epochs=max_epochs, batch_size=batch_size, verbose=2,
      callbacks=[reduce_lr,early_stopping])

model.save('model_file.h5')