将 InputLayer 添加到现有 Keras 模型以与 Android 张量流库一起使用

Question

我是 keras 和 TensorFlow 的新手，

在尝试将 Keras 模型（在新的 iOS IA 框架上编译和正常工作）转换为要在 Android 中使用的 tensorflow 模型时，我缺少输入节点。

因此，我试图将 InputLayer 添加到我的模型中，但没有成功。

我得到的错误如下（每次运行时占位符编号不同...）：

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_159' with dtype float
     [[Node: Placeholder_159 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

这是我的代码，与工作中的 KERAS 模型相比，修改的部分是这个标签之间的几行 #######################################

# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = '/train'
validation_data_dir = '/validation'
nb_train_samples = 120 #target 2700
nb_validation_samples = 24 #target 600
epochs = 20 #target 50
batch_size = 15 #target 30

if K.image_data_format() == 'channels_first':

    input_shape = (None, 3, img_width, img_height)

else:

    input_shape = (None, img_width, img_height, 3)



##########################
#
#  THIS IS THE CODE FOR INTRODUCING THE INPUT LAYER

# To create the input layer Instanciate an input placeholder
inputp = tensorflow.placeholder(tensorflow.float32, shape=input_shape) 

model = Sequential()

# ADD the input layer as the first layer of the model
model.add(InputLayer(input_tensor=inputp, input_shape=input_shape)) 

#the working code without the input layer was (input_shape without the None dimension):
# model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Conv2D(32, (3, 3)))

#
#  THE REST OF THE CODE IS IDENTICAL TO THE WORKING KERAS MODEL
##########################

model.add(Activation('relu'))    
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Conv2D(32, (3, 3)))    
model.add(Activation('relu'))    
model.add(MaxPooling2D(pool_size=(2, 2)))    
model.add(Conv2D(64, (3, 3)))   
model.add(Activation('relu'))    
model.add(MaxPooling2D(pool_size=(2, 2)))    
model.add(Flatten())    
model.add(Dense(64))    
model.add(Activation('relu'))    
model.add(Dropout(0.5))    
model.add(Dense(3))    
model.add(Activation('softmax')) #use sigmoid when binary and softmax when categorical

model.compile(loss='categorical_crossentropy',    
              optimizer='rmsprop',    
              metrics=['accuracy'])

# this is the augmentation configuration we will use for training

train_datagen = ImageDataGenerator(    
    rescale=1. / 255,    
    shear_range=0.2,    
    zoom_range=0.2,   
    horizontal_flip=True)

# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(    
    train_data_dir,    
    target_size=(img_width, img_height),    
    batch_size=batch_size,    
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(    
    validation_data_dir,    
    target_size=(img_width, img_height),    
    batch_size=batch_size,    
    class_mode='categorical')

model.fit_generator(  
    train_generator,    
    steps_per_epoch=nb_train_samples // batch_size,    
    epochs=epochs,    
    validation_data=validation_generator,    
    validation_steps=nb_validation_samples // batch_size)


model.save('androidtest_model.h5')
model.save_weights('androidtest_weights.h5')

Answer 1

使用 Keras API，您不必使用 Tensor 来输入数据。

首先设置扁平化的输入形状：

# Start construction of the Keras Sequential model.
model = Sequential()

# Add an input layer which is similar to a feed_dict in TensorFlow.
# Note that the input-shape must be a tuple containing the image-size.
model.add(InputLayer(input_shape=(3*img_width*img_height)))

# Add the real shape that conv layer spect
model.add(Reshape((img_width,img_height , 3)))

然后训练网络将图像作为 numpy 数组传递，它类似于 scikitlearn

# Note that train_datagen and train_labels must be numpy array object
model.fit(x=train_datagen,
          y=train_labels,
          epochs=1, batch_size=128)