我做了一个自定义生成器，在训练期间，我需要我的模型的预测，在它被训练到真正的标签之前，对它做一些计算。因此，我先保存模型，然后在当前状态上调用model.predict（）。
from keras.models import load_model
def custom_generator(model):
while True:
state, target_labels = next(train_it)
model.save('my_model.h5')
#pause training and do some calculations on the output of the model trained so far
print(state)
print(target_labels)
model.predict(state)
#resume training
#model = load_model('my_model.h5')
yield state, target_labels
model3.fit_generator(custom_generator(model3), steps_per_epoch=1, epochs = 10)
loss = model3.evaluate_generator(test_it, steps=1)
loss
由于调用model，我得到了以下错误。在自定义\u生成器（）中预测（模型）
错误：
ValueError:Tensor Tensor（“dense_2/Softmax:0”，shape=（？，200），dtype=float32）不是此图的元素。
请帮助我如何在训练期间在自定义生成器中获得模型预测（或最后一层输出）。
这是我的模型：
#libraries
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from matplotlib import pyplot
from keras.applications.vgg16 import VGG16
model = VGG16(include_top=False, weights='imagenet')
print(model.summary())
#add layers
z = Conv2D(1, (3, 3), activation='relu')(model.output)
z = Conv2D(1,(1,1), activation='relu')(z)
z = GlobalAveragePooling2D()(z)
predictions3 = Dense(200, activation='softmax')(z)
model3 = Model(inputs=model.input, outputs=predictions3)
for layer in model3.layers[:20]:
layer.trainable = False
for layer in model3.layers[20:]:
layer.trainable = True
model3.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')
用于加载训练和测试数据的图像数据生成器
from keras.preprocessing.image import ImageDataGenerator
# create a data generator
datagen = ImageDataGenerator()
# load and iterate training dataset
train_it = datagen.flow_from_directory('DATA/C_Train/', class_mode='categorical', batch_size=1)
test_it = datagen.flow_from_directory('DATA/C_Test/', class_mode='categorical', batch_size=1)
您的最佳选择可能是通过批处理train\u或fit编写自定义列车循环；只有当使用_multiprocessing=True或使用回调时，前者才处于不利地位——事实并非如此。下面是一个使用批上训练的实现-如果改用fit（用于多处理、回调等），请确保一次只反馈一个批，并且不提供任何验证数据（改用模型。评估）-否则控制流中断。（此外，自定义回调是一种有效的、但涉及的替代方法）
iters_per_epoch = len(train_it) // batch_size
num_epochs = 5
outs_store_freq = 20 # in iters
print_loss_freq = 20 # in iters
iter_num = 0
epoch_num = 0
model_outputs = []
loss_history = []
while epoch_num < num_epochs:
while iter_num < iters_per_epoch:
x_train, y_train = next(train_it)
loss_history += [model3.train_on_batch(x_train, y_train)]
x_test, y_test = next(test_it)
if iter_num % outs_store_freq == 0:
model_outputs += [model3.predict(x_test)]
if iter_num % print_loss_freq == 0:
print("Iter {} loss: {}".format(iter_num, loss_history[-1]))
iter_num += 1
print("EPOCH {} FINISHED".format(epoch_num + 1))
epoch_num += 1
iter_num = 0 # reset counter
from keras.models import Sequential
from keras.layers import Dense, Conv2D, GlobalAveragePooling2D
from keras.models import Model
from keras.optimizers import SGD
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator
model = VGG16(include_top=False, weights='imagenet')
print(model.summary())
#add layers
z = Conv2D(1, (3, 3), activation='relu')(model.output)
z = Conv2D(1,(1,1), activation='relu')(z)
z = GlobalAveragePooling2D()(z)
predictions3 = Dense(2, activation='softmax')(z)
model3 = Model(inputs=model.input, outputs=predictions3)
for layer in model3.layers[:20]:
layer.trainable = False
for layer in model3.layers[20:]:
layer.trainable = True
model3.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='categorical_crossentropy')
batch_size = 1
datagen = ImageDataGenerator()
train_it = datagen.flow_from_directory('DATA/C_Train/',
class_mode='categorical',
batch_size=batch_size)
test_it = datagen.flow_from_directory('DATA/C_Test/',
class_mode='categorical',
batch_size=batch_size)
[自定义火车回路]
奖金代码：要获得任何层的输出，请使用以下内容：
def get_layer_outputs(model, layer_name, input_data, learning_phase=1):
outputs = [layer.output for layer in model.layers if layer_name in layer.name]
layers_fn = K.function([model.input, K.learning_phase()], outputs)
return [layers_fn([input_data,learning_phase])][0]
outs = get_layer_outputs(model, 'dense_1', x_test, 0) # 0 == inference mode
