如何在Tensorflow中保存多层感知器模型

我正在尝试保存以下文件，但不确定如何保存。我尝试with tf.Session as sess在训练模型之前就放置了，history = model.fit_generator...但是正在收到ValueError: No variables to save。然后，我尝试将with tf.Session...模型初始化放在上方model = Sequential()。我是Tensorflow的新手，所以我只是想学习绳索。

任何指导都会很棒，谢谢！

import numpy as np
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Activation, Dropout, Input
from keras.layers.core import Dense, Flatten
from keras.optimizers import Adam, Adadelta, SGD
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
from sklearn.metrics import confusion_matrix
from keras.models import Model
from keras.utils import np_utils
import itertools
import matplotlib.pyplot as plt
import livelossplot
#%matplotlib inline
#plot_losses = livelossplot.PlotLossesKeras()

PATH = './Food-5K/'

train_path = '%straining/' %PATH
valid_path = '%svalidation/' %PATH
test_path = '%sevaluation/' %PATH

classes = ('food', 'non-food')

print (train_path)

batch_size = 16
epochs = 20
nb_train_samples = 3001
nb_validation_samples = 1000

train_batches = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True).flow_from_directory(train_path, target_size=(224,224),
        batch_size=32, class_mode='binary')
valid_batches = ImageDataGenerator(rescale = 1./255 ).flow_from_directory(valid_path, target_size=(224,224), 
        batch_size=batch_size, class_mode='binary')
test_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(test_path, target_size=(224,224), 
        batch_size=batch_size, class_mode='binary')

print(type(train_batches[0]))

x_train, y_train = train_batches[0]
x_test, y_test = valid_batches[0]

print('x_train.shape: ' + str(x_train.shape))
print('y_train.shape: ' + str(y_train.shape))
print('y_train.shape: ' + str(y_train.reshape(y_train.shape + (1,)).shape))

print('x_test.shape: ' + str(x_test.shape))
print('y_test.shape: ' + str(y_test.shape))
print('y_test.shape: ' + str(y_test.reshape(y_test.shape + (1,)).shape))

X_train_flatten = x_train.reshape(x_train.shape[0], -1).T
X_test_flatten = x_test.reshape(x_test.shape[0], -1).T

y_train_flatten = y_train.T
y_test_flatten = y_test.T

print('X_train_flatten.shape: ' + str(X_train_flatten.T.shape))
print('y_train_flatten.shape: ' + str(y_train_flatten.shape))
#print('y_train_flatten.shape: ' + str(np.squeeze(y_train_flatten, axis=(2,)).shape))

print('X_test_flatten.shape: ' + str(X_test_flatten.T.shape))
print('y_test_flatten.shape: ' + str(y_test_flatten.shape))
#print('y_test_flatten.shape: ' + str(np.squeeze(y_test_flatten, axis=(2,)).shape))

train_set_x = X_train_flatten/255.
test_set_x = X_test_flatten/255.

print('len(train_set_x): ' + str(train_set_x.shape))
print('len(test_set_x): ' + str(test_set_x.shape))

print(y_train.shape)

# plots images with labels within jupyter notebook
def plots(ims, figsize=(80,60), rows=1, interp=False, titles=None):
    if type(ims[0]) is np.ndarray:
        #print(ims[0])
        #ims = np.array(ims).astype(np.uint8)
        #print(ims)

        if (ims.shape[-1] != 3):
            ims = ims.transpose((1,2,3,1))
    f = plt.figure(figsize=figsize)
    cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1
    for i in range(len(ims)):
        sp = f.add_subplot(rows, cols, i+1)
        sp.axis('Off')
        if titles is not None:
            sp.set_title(titles[i], fontsize=15)
        plt.imshow(ims[i], interpolation=None if interp else 'none')

imgs, labels = next(train_batches)
plots(imgs, titles=labels)

# Deep Multilayer Perceptron model
model = Sequential()
# Set the initial random weights > kernel_initializer
model.add(Flatten(input_shape=(224, 224, 3)))
model.add(Dense(200, input_dim=150528, kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(100, input_dim=200, kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10, input_dim=100, kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1, input_dim=10, kernel_initializer='normal'))
model.add(Activation('sigmoid'))
# Rho > is a hyper-parameter which attenuates the influence of past gradient.
model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
model.summary()

# Train
history = model.fit_generator(train_batches, steps_per_epoch=32, #steps_per_epoch=nb_train_samples, 
                        #callbacks=[plot_losses],
                            validation_steps = 16,
                    validation_data=train_batches, epochs=epochs, verbose=1)

# Evaluate
x_test, y_test = valid_batches[0]
evaluation = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))

print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()