Question

我正在尝试使用MNIST数据集上的InceptionV3进行迁移学习。

计划是读取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，当我运行此命令时，错误停止在model.fit()上，

ValueError：检查输入时出错：预期inception_v3_input具有4维，但数组的形状为（299，299，3）

这是怎么回事？

Answer 1

在将map应用到数据集后，响应没有有关批处理大小的信息，您必须调用batch函数将其添加：

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进行全面的训练和评估，就像您检查here一样。

TensorFlow数据集的意外尺寸

1 个答案: