将代码从 TFLearn 转换为在 Keras 中工作

Question

我正在使用一些用 TFLearn 编写的代码作为参考，并尝试使用 Keras 重写它。我对这两个包都很陌生，我不确定我是否写得正确。

我已经尝试了我的代码 - 它有效 - 但我没有得到预期的结果（准确度没有提高超过 20+ 个纪元）我想知道我是否在某处犯了错误。

就我的数据而言，我有一个 'data' 目录，其中包含 'train' 和 'validation' 目录。在每一个里面，我的 3 张图片都有 3 个目录类.

原始 TFLearn 代码：

import numpy as np
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression

def createModel(nbClasses,imageSize):

    convnet = input_data(shape=[None, imageSize, imageSize, 1], name='input')

    convnet = conv_2d(convnet, 64, 2, activation='elu', weights_init="Xavier")
    convnet = max_pool_2d(convnet, 2)

    convnet = conv_2d(convnet, 128, 2, activation='elu', weights_init="Xavier")
    convnet = max_pool_2d(convnet, 2)

    convnet = conv_2d(convnet, 256, 2, activation='elu', weights_init="Xavier")
    convnet = max_pool_2d(convnet, 2)

    convnet = conv_2d(convnet, 512, 2, activation='elu', weights_init="Xavier")
    convnet = max_pool_2d(convnet, 2)

    convnet = fully_connected(convnet, 1024, activation='elu')
    convnet = dropout(convnet, 0.5)

    convnet = fully_connected(convnet, nbClasses, activation='softmax')
    convnet = regression(convnet, optimizer='rmsprop', loss='categorical_crossentropy')

    model = tflearn.DNN(convnet)
return model

我使用 Keras 的代码：

from keras import backend as K
from keras.layers.core import Flatten, Dense, Dropout, Activation
from keras.optimizers import rmsprop
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
import numpy as np

num_classes = 3
image_size = 256
nb_epoch = 80
batch_size = 32
nb_train_samples = 7994
nb_validation_samples = 2000

if K.image_data_format() == 'channels_first':
    input_shape = (3, image_size, image_size)
else:
    input_shape = (image_size, image_size, 3)

model = Sequential()

model.add(ZeroPadding2D((1,1), input_shape=input_shape))

model.add(Conv2D(64, 2, activation='elu', kernel_initializer='glorot_normal'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(128, 2, activation='elu', kernel_initializer='glorot_normal'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(256, 2, activation='elu', kernel_initializer='glorot_normal'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(512, 2, activation='elu', kernel_initializer='glorot_normal'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('elu'))
model.add(Dropout(0.5))

model.add(Dense(num_classes))
model.add(Activation('softmax'))
opt = rmsprop()
model.compile(loss='categorical_crossentropy',
         optimizer = opt,
         metrics = ['accuracy'])

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
train_datagen = ImageDataGenerator(rescale= 1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)

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

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(image_size, image_size),
    batch_size=batch_size,
    class_mode='categorical'
    )



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

model.save_weights('first_try.h5')

Answer 1

我用 MNIST 数据集的三个类尝试了你的代码，并且可以很好地训练。正如预期的那样，准确度在第一个时期内增加。

至少对于 MNIST，我可以通过仅使用前两个 Conv 层和一个 64 层的密集层来训练得更快。根据您的数据，我建议您尝试使用更简单的模型（即 2 个 Conv 层），检查是否该模型正在学习，然后从那里改进。

将代码从 TFLearn 转换为在 Keras 中工作

Converting code from TFLearn to work in Keras

python

keras

tflearn