def build_3dcnn_model(self, fusion_type, Fusion):
if len(Fusion[0]) == 1:
input_shape = (32, 32, len(Fusion))
model_in, model = self.cnn_2D(input_shape)
else:
input_shape = (32, 32, 5, len(Fusion))
model_in, model = self.cnn_3D(input_shape)
model = Dropout(0.5)(model)
model = Dense(32, activation='relu', name='fc2')(model)
model = Dense(self.config.classes, activation='softmax',
name='fc3')(model)
model = Model(input=model_in, output=model)
# 统计参数
# model.summary()
plot_model(model,
to_file='experiments/img/' + str(Fusion) + fusion_type +
r'_model.png',
show_shapes=True)
print ' Saving model Architecture'
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
# model.compile(optimizer=adam, loss=self.mycrossentropy, metrics=['accuracy']) #有改善，但不稳定
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
jaw = Dropout(rate=0.2)(jaw)
jaw = Flatten()(jaw)
jaw = Dense(32, activation='relu')(jaw)
jaw = Dense(32, activation='relu')(jaw)
jaw = Dense(7, activation='softmax')(jaw)
jaw = Model(inputs, jaw)'''
import csv
import keras
if mode == "train":
es_callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
#we need only one ytrain and ytest so we must to organise our dataset
jaw.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(lr=0.00001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=1e-6,
amsgrad=False),
metrics=['acc'])
history = jaw.fit(xtrain7,
ytrain7,
callbacks=[es_callback],
validation_data=(xtest7, ytest7),
epochs=200,
batch_size=16)
#on plot les resultats pour vérifier les performances du réseau
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
)'''
print('---------------------{}'.format(xtest5.shape))
print('---------------------{}'.format(xtest7.shape))
#we need only one ytrain and ytest so we must to organise our dataset
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
history1 = model.fit(
[xtrain1, xtrain2, xtrain3, xtrain4, xtrain5, xtrain6, xtrain7],
ytrain,
validation_data=([xtest1, xtest2, xtest3, xtest4, xtest5, xtest6,
xtest7], ytest),
epochs=2000,
batch_size=2)
'''mouth.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
history1 = mouth.fit_generator(datagen.flow(xtrain5, ytrain5, batch_size=2), epochs=25,
validation_data=(xtest5, ytest5), steps_per_epoch=xtrain5.shape[0]//2)
jaw.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
history2 = jaw.fit_generator(datagen.flow(xtrain7, ytrain7, batch_size=2), epochs=25,
validation_data=(xtest7, ytest7), steps_per_epoch=xtrain7.shape[0]//2)
'''
'''###########################################Model Final
result = jaw*0.6 + mouth*0.4
model_final_dense_1 = Dense(7, activation='softmax')
