我对 keras 和 TensorFlow 非常陌生,
当尝试将 Keras 模型(在新的 iOS IA 框架上编译并正常工作)转换为要在 Android 中使用的 tensorflow 模型时,我缺少输入节点。
因此,我尝试将 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都不同......):
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 KERAS 模型相比,修改的部分是此标签之间的几行######################## ################
# 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')
最佳答案
使用 Keras API,您不必使用 Tensor 来输入数据。
首先设置展平的输入形状:
# 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)))
然后将图像作为 numpy 数组传递来训练网络,这与 scikitlearn 类似
# 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)
关于python - 将 InputLayer 添加到现有 Keras 模型以与 Android Tensor Flow Library 一起使用,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/45286957/