TensorFlow 数据集的意外维度答案

【问题标题】：Unexpected dimensions with TensorFlow datasetTensorFlow 数据集的意外维度
【发布时间】：2020-01-28 05:55:49
【问题描述】：

我正在尝试在 MNIST 数据集上使用 InceptionV3 进行迁移学习。

计划是读入 MNIST 数据集，调整图像大小，然后使用它们进行训练，如下所示：

import numpy as np
import os
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

import tensorflow.compat.v2 as tf
import tensorflow.compat.v1 as tfv1
from tensorflow.python.keras.applications import InceptionV3

tfv1.enable_v2_behavior()

print(tf.version.VERSION)

img_size = 299

def preprocess_tf_image(image, label):
  image = tf.image.grayscale_to_rgb(image)
  image = tf.image.resize(image, [img_size, img_size])
  return image, label

#Acquire MNIST data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#Convert data to [0,1] range
x_train, x_test = x_train / 255.0, x_test / 255.0

#Add extra dimension to images so that they can be converted to RGB
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test  = x_test.reshape (x_test.shape[0],  28, 28, 1)

x_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
x_test  = tf.data.Dataset.from_tensor_slices((x_test, y_test))

#Convert images to RGB space and resize
x_train = x_train.map(preprocess_tf_image)
x_test  = x_test.map(preprocess_tf_image)

img_shape = (img_size, img_size, 3)

#Get trained model, but leave off the head
base_model = InceptionV3(input_shape = img_shape, weights='imagenet', include_top=False)
base_model.trainable = False

#Make a model with a new head
model = tf.keras.Sequential([
  base_model,
  tf.keras.layers.GlobalAveragePooling2D(),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

#Compile model
model.compile(
    optimizer='adam', #tf.keras.optimizers.RMSprop(lr=BASE_LEARNING_RATE),
    loss='binary_crossentropy',
    metrics=['accuracy']
)

model.fit(x_train, epochs=5)

model.evaluate(x_test)

但是，当我运行它时，事情会在 model.fit() 处停止并出现错误：

ValueError：检查输入时出错：预期 inception_v3_input 有 4 维，但得到的数组形状为 (299, 299, 3)

发生了什么事？

【问题讨论】：

标签： python tensorflow machine-learning tensorflow-datasets mnist

【解决方案1】：

将map 应用到数据集后，响应中没有关于批量大小的信息，您必须调用batch 函数来添加它：

x_train = x_train.batch(batch_size = BATCH_SIZE) # adds batch size dimension to train dataset

x_test = x_test.batch(batch_size = BATCH_SIZE) # idem for test.

之后我可以使用 Google 的 Colab 对模型进行全面训练和评估，您可以查看 here。

【讨论】：