Question

我正在使用以下代码：

import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten,\
 Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
np.random.seed(1000)


# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer

model.add(Conv2D(kernel_size=96, filters=(11, 11), input_shape=(64,64,3), activation='relu', strides=(4,4), padding='valid'))
# Pooling 
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation before passing it to the next layer
model.add(BatchNormalization())

# 2nd Convolutional Layer
model.add(Conv2D(256, 11, 11, activation='relu', strides=(1,1), padding='valid'))

# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# 3rd Convolutional Layer
model.add(Conv2D(384, 3, 3, activation='relu', strides=(1,1), padding='valid'))

# Batch Normalisation
model.add(BatchNormalization())

# 4th Convolutional Layer
model.add(Conv2D(384, 3, 3, activation='relu', strides=(1,1), padding='valid'))

# Batch Normalisation
model.add(BatchNormalization())

# 5th Convolutional Layer
model.add(Conv2D(256, 3, 3, activation='relu', strides=(1,1), padding='valid'))

# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(4096, input_shape=(224*224*3,)))
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# 2nd Dense Layer
model.add(Dense(4096))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# 3rd Dense Layer
model.add(Dense(1000))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

output_node=109
# Output Layer
model.add(Dense(output_node.shape, activation='softmax'))


model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam',\
 metrics=['accuracy'])

#Fitting dataset

from keras.preprocessing.image import ImageDataGenerator

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

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'categorical')
#steps_per_epoch = number of images in training set / batch size (which is 55839/32)
#validation_steps = number of images in test set / batch size (which is 18739/32)

classifier.fit_generator(
        training_set,
        steps_per_epoch=55839/32,
        epochs=5,
        validation_data=test_set,
        validation_steps=18739/32)

我收到此错误：

TypeError: only size-1 arrays can be converted to Python scalars

我尝试查找此解决方案：Keras Model giving TypeError: only size-1 arrays can be converted to Python scalars 但是，您可以看到，我在输出层中使用了.shape方法，但仍然无法正常工作。我看不到要在哪里创建数组，该数组必须是行中大小为1的数组

model.add(Conv2D(kernel_size=96, filters=(11, 11), input_shape=(64,64,3), activation='relu', strides=(4,4), padding='valid'))

因为这是触发错误的地方。

编辑：我试图按照@TavoGLC的建议为“过滤器”设置一个整数值：

model.add(Conv2D(filters=11, kernel_size=96, input_shape=(224,224,3), activation='relu', strides=(4,4), padding='valid', data_format='channels_last'))

，我添加了data_format ='channels_last'来解决负值问题。这使这行代码正常运行，但是第二卷积层开始给我带来问题。

# 2nd Convolutional Layer
model.add(Conv2D(filters=11, kernel_size=256, strides=(1,1), padding='valid', activation='relu'))

错误：

ValueError: Negative dimension size caused by subtracting 256 from 16 for 'conv2d_77/convolution' (op: 'Conv2D') with input shapes: [?,33,16,5], [256,256,33,11].

我再次尝试了此处提供的解决方案：Negative dimension size caused by subtracting 3 from 1 for 'conv2d_2/convolution' 似乎什么都没用。

Answer 1

更改这些：

过滤器-使用单个整数（卷积输出过滤器的数量）。
kernel_size -使用较小的大小，因为内核必须按照输入形状移动（对于较深的图层，形状可能会减小，因此您必须了解图层输入的形状才能获取大小）
其他卷积层-您必须使用元组（例如Conv2D(256, (11, 11))），否则它将被视为另一个变量，对于所有Conv2D，请遵循前面关于filter和kernel_size的过程层。
对于输出形状使用

output_node=109
# Output Layer
model.add(Dense(output_node, activation='softmax'))

在keras模型中获取``仅大小为1的数组可以转换为python标量''错误

1 个答案: