为 ResNet50 重塑 MNIST

Question

我正在尝试使用 Keras 库在 ResNet50 上训练 mnist 数据集。 mnist 的形状是 (28, 28, 1) 然而 resnet50 要求形状是 (32, 32, 3)

如何将 mnist 数据集转换为所需的形状？

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)
x_train = x_train/255.0
x_test = x_test/255.0

from keras.utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

model = models.Sequential()
# model.add(InputLayer(input_shape=(28, 28)))
# model.add(Reshape(target_shape=(32, 32, 3)))
# model.add(Conv2D())
model.add(conv_base)
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(10, activation='softmax'))

model.compile(optimizer=optimizers.RMSprop(lr=2e-5), loss='binary_crossentropy', metrics=['acc'])

history = model.fit(x_train, y_train, epochs=5, batch_size=20, validation_data=(x_test, y_test))

ValueError: Input 0 is incompatible with layer sequential_10: expected shape=(None, 32, 32, 3), found shape=(20, 28, 28, 1)

Answer 1

您需要调整 MNIST 数据集的大小。请注意，最小尺寸实际上取决于 ImageNet 模型。例如：Xception 至少需要 72，其中 ResNet 要求 32。除此之外，MNIST 是一个灰度图像，但如果您使用这些模型的预训练权重，它可能会发生冲突。因此，好的和安全的一面是调整大小并将灰度转换为 RGB.

完整的工作代码。

数据集

我们会将 MNIST 的大小从 28 调整为 32。此外，制作 3 个通道而不是保留 1 个。

import tensorflow as tf 
import numpy as np 

(x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()

# expand new axis, channel axis 
x_train = np.expand_dims(x_train, axis=-1)

# [optional]: we may need 3 channel (instead of 1)
x_train = np.repeat(x_train, 3, axis=-1)

# it's always better to normalize 
x_train = x_train.astype('float32') / 255

# resize the input shape , i.e. old shape: 28, new shape: 32
x_train = tf.image.resize(x_train, [32,32]) # if we want to resize 

# one hot 
y_train = tf.keras.utils.to_categorical(y_train , num_classes=10)

print(x_train.shape, y_train.shape)

(60000, 32, 32, 3) (60000, 10)

ResNet 50

input = tf.keras.Input(shape=(32,32,3))
efnet = tf.keras.applications.ResNet50(weights='imagenet',
                                             include_top = False, 
                                             input_tensor = input)
# Now that we apply global max pooling.
gap = tf.keras.layers.GlobalMaxPooling2D()(efnet.output)

# Finally, we add a classification layer.
output = tf.keras.layers.Dense(10, activation='softmax', use_bias=True)(gap)

# bind all
func_model = tf.keras.Model(efnet.input, output)

火车

func_model.compile(
          loss  = tf.keras.losses.CategoricalCrossentropy(),
          metrics = tf.keras.metrics.CategoricalAccuracy(),
          optimizer = tf.keras.optimizers.Adam())
# fit 
func_model.fit(x_train, y_train, batch_size=128, epochs=5, verbose = 2)

Epoch 1/5
469/469 - 56s - loss: 0.1184 - categorical_accuracy: 0.9690
Epoch 2/5
469/469 - 21s - loss: 0.0648 - categorical_accuracy: 0.9844
Epoch 3/5
469/469 - 21s - loss: 0.0503 - categorical_accuracy: 0.9867
Epoch 4/5
469/469 - 21s - loss: 0.0416 - categorical_accuracy: 0.9888
Epoch 5/5
469/469 - 21s - loss: 0.1556 - categorical_accuracy: 0.9697
<tensorflow.python.keras.callbacks.History at 0x7f316005a3d0>

为 ResNet50 重塑 MNIST

Reshaping MNIST for ResNet50

python

tensorflow

keras

mnist

resnet