如何将 MNIST 训练图像从 (60000, 28, 28) 重塑为 (60000, 16, 16)？

Question

我正在尝试使用 Keras 学习具有简单密集层的 MNIST 数据集。我希望我的图像大小为 16*16 而不是 28*28。我用了很多方法，但都不管用。这是简单的密集网络：

import keras
import numpy as np
import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical

train_images = mnist.train_images()
train_labels = mnist.train_labels()
test_images = mnist.test_images()
test_labels = mnist.test_labels()

# Normalize the images.
train_images = (train_images / 255) - 0.5
test_images = (test_images / 255) - 0.5
print(train_images.shape)
print(test_images.shape)

# Flatten the images.
train_images = train_images.reshape((-1, 784))
test_images = test_images.reshape((-1, 784))
print(train_images.shape)
print(test_images.shape)
# Build the model.
model = Sequential([
    Dense(10, activation='softmax', input_shape=(784,)),
])
# Compile the model.
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy'],
)

# Train the model.
model.fit(
    train_images,
    to_categorical(train_labels),
    epochs=5,
    batch_size=32,
)

# Evaluate the model.
model.evaluate(
    test_images,
    to_categorical(test_labels)
)

# Save the model to disk.
model.save_weights('model.h5')

Answer 1

试试这个方法一次调整所有图片的大小 -

#!pip install --upgrade tensorflow
#Assuming you are using tensorflow 2

import numpy as np
import tensorflow as tf

#creating dummy images
imgs = np.stack([np.eye(28), np.eye(28)])
print(imgs.shape)
#Output - (2,28,28) 2 images of 28*28


imgt = imgs.transpose(1,2,0)  #Bring the batch channel to the end (28,28,2)
imgs_resize = tf.image.resize(imgt, (16,16)).numpy() #apply resize (14,14,2)
imgs2 = imgs_resize.transpose(2,0,1) #bring the batch channel back to front (2,14,14)
print(imgs2.shape)
#Output - (2,16,16)

如何将 MNIST 训练图像从 (60000, 28, 28) 重塑为 (60000, 16, 16)？

how to reshape the MNIST train images from (60000, 28, 28) to (60000, 16, 16)?

python

reshape

mnist

keras