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python - 如何在 keras 中使用 cifar100 实现denseNet 架构？

问题描述

如何在 Keras 中使用 cifar100 实现denseNet 架构？我看到 Keras 中的密集网络仅使用 imageNet 实现！如何使用 cifar100 实现

标签： pythonkerasdeep-learning

解决方案

以下示例将帮助您了解如何cifar100使用DenseNet121. 请注意，我使用keraswith in tensorflow。

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras import backend as K

# import cifar 100 data
# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar100.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# create the base pre-trained model
base_model = DenseNet121(weights='imagenet', include_top=False)

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(100)(x)

# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)

# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional layers
for layer in base_model.layers:
    layer.trainable = False

# compile the model (should be done *after* setting layers to non-trainable)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='rmsprop', loss=loss, metrics=['accuracy'])

# train the model on the new data for a few epochs
model.fit(x_train,y_train,epochs=5, validation_data=(x_test,y_test), verbose=1,batch_size=128)

您也可以进行微调，因为我训练了将原始base_model权重保持在冻结状态的模型（未训练原始 base_model 的权重）。在微调期间，您可以解冻一些层并再次训练。我还建议您阅读有关ImageDataGenerator增强图像并在测试期间获得更好的准确性的信息。

希望能帮助到你。

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