如何在多个数据集上正确实现 Keras 的 fit_generator？

Question

我在实现 Keras 的 fit_generator 函数时遇到问题。我已经在线关注了 Keras 文档和许多其他文档。但我似乎无法让这件事发挥作用。

当我运行 fit_generator 时，它不会引发错误。我可以说有些东西在后台运行，因为我的任务管理器上的 GPU 使用率飙升至 70% 的处理。但是，没有文本/详细说明正在为我的卷积神经网络处理批次。

这是我的模型

import keras
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential

model = Sequential()

model.add(Conv2D(filters=80, kernel_size=4, strides=1, activation='relu', input_shape=(180, 180, 3)))
model.add(Dropout(rate = 0.2))
model.add(MaxPooling2D(pool_size=2, strides=2))
model.add(Conv2D(filters=60, kernel_size=2, strides=1, activation='relu'))
model.add(Dropout(rate = 0.2))
model.add(MaxPooling2D(pool_size=2, strides=2))
model.add(Dense(units = 40, activation = 'relu'))
model.add(Dense(units = 20, activation = 'relu'))
model.add(Flatten())
model.add(Dense(units=5270, activation='softmax'))

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

这是我的批处理生成器

我有六个要循环的 hdf5 文件，每个文件包含 40,000 个图像。它们已经被格式化为 Numpy 数组。我每次产生 20 个批量大小。

def train_generator():
    counter = 1
    batch_size = 20

    while True:

        # Create arrays to contain x_train and y_train. There are six of these files in total, so 40000*6 = 240,000 items in the entire training set.
        # 240,000 images for each epoch
        h5f = h5py.File('x_train' + str(counter) + 'catID.h5','r')
        pic_arr = h5f['dataset'][0:40000]

        h5f = h5py.File('y_train' + str(counter) + 'catID.h5','r')
        cat_arr = h5f['dataset'][0:40000]
        h5f.close()

        # Since training size for first dataset is 40,000 and batch_size is 20, loop 2000 times because 40000/20 = 2000 
        for i in range(1,2001):
            if (i == 1):
                x_train = pic_arr[0:batch_size]
                y_train = cat_arr[0:batch_size]

                index = batch_size
                yield (x_train, y_train)
            else:
                x_train = pic_arr[index:index + batch_size]
                y_train = cat_arr[index:index + batch_size]

                index += batch_size
                yield (x_train, y_train)

        del pic_arr
        del cat_arr
        counter += 1

适合我的模型

当用我的生成器拟合我的模型时，我知道我的 GPU 正在处理数据；我有一个 NVIDIA GTX 1070。但是在下面运行此代码时没有显示详细/文本。我也尝试在没有 GPU 的情况下运行，但仍然没有运气。我在这里做错了吗？

from keras.callbacks import ModelCheckpoint
import tensorflow as tf

# This is used to store the best weights for our trained model.
checkpointer = ModelCheckpoint(filepath='weights_bestcatID.hdf5', 
                           verbose=1, save_best_only=True)

# steps_per_epoch=12000 because --> 240,000 (total samples) / 20 (batch size) = 12000 
with tf.device('/device:GPU:0'):
    model.fit_generator(train_generator(), steps_per_epoch=12000, nb_epoch=4, verbose = 1, callbacks=[checkpointer])

Answer 1

没关系。我尝试再次运行相同的代码并且它工作......如果有人需要参考如何实现 Keras 的 fit_generator，上述工作。