我已经通过现有的q＆a查看了这类问题，但是无法解决这个问题。当我使用VGG模型拟合我的图像分类器模型时，我收到以下错误消息：
检查输入时出错：期望input_1有4个维度，但是有形状的数组（无，无，224,224,3）
这是我到目前为止使用的代码：
!wget -qq https://www.dropbox.com/s/9gc4fr1uiveedn6/simpsons.zip?dl=0
!unzip -qq simpsons.zip?dl=0
!rm simpsons.zip?dl=0import glob
import pandas as pd
filenames_n0 = glob.glob('./simpsons/homer /*.jpg')
filenames_n1 = glob.glob('./simpsons/marge /*.jpg')
filenames_n2 = glob.glob('./simpsons/bart /*.jpg')
filenames_n3 = glob.glob('./simpsons/lisa /*.jpg')
filenames_n4 = glob.glob('./simpsons/maggie /*.jpg')
names = ['homer', 'marge','bart', 'lisa', 'maggie']
!ls
df = pd.DataFrame(filenames_n0, columns = ["filename"])
df1 = pd.DataFrame(filenames_n1, columns = ["filename"])
df2 = pd.DataFrame(filenames_n2, columns = ["filename"])
df3 = pd.DataFrame(filenames_n3, columns = ["filename"])
df4 = pd.DataFrame(filenames_n4, columns = ["filename"])
df['class'] = pd.Series([0 for x in range(len(df.index))], index=df.index)
df1['class'] = pd.Series([1 for x in range(len(df1.index))], index=df1.index)
df2['class'] = pd.Series([2 for x in range(len(df2.index))], index=df2.index)
df3['class'] = pd.Series([3 for x in range(len(df3.index))], index=df3.index)
df4['class'] = pd.Series([4 for x in range(len(df4.index))], index=df4.index)
train_set_percentage = .9
train_df = df[:int(len(df)*train_set_percentage)]
val_df = df[int(len(df)*train_set_percentage):]
train_df1 = df1[:int(len(df1)*train_set_percentage)]
val_df1 = df1[int(len(df1)*train_set_percentage):]
train_df2 = df2[:int(len(df2)*train_set_percentage)]
val_df2 = df2[int(len(df2)*train_set_percentage):]
train_df3 = df3[:int(len(df3)*train_set_percentage)]
val_df3 = df3[int(len(df3)*train_set_percentage):]
train_df4 = df4[:int(len(df4)*train_set_percentage)]
val_df4 = df4[int(len(df4)*train_set_percentage):]
#concat train & val datasets to form 1 of each.
df_new_train = pd.concat([train_df, train_df1, train_df2, train_df3, train_df4])
df_new_val = pd.concat([val_df, val_df1, val_df2, val_df3, val_df4])
df = df_new_train.sample(frac=1).reset_index(drop=True)
df_val = df_new_val.sample(frac=1).reset_index(drop=True)
train_filenames_list = df["filename"].tolist()
train_labels_list = df['class'].astype('int32').tolist()
val_filenames_list = df_val["filename"].tolist()
val_labels_list = df_val['class'].astype('int32').tolist()
num_classes = 5
train_set_percentage = .9
train_filenames_list = train_filenames_list[:int(len(train_filenames_list)*train_set_percentage)]
train_labels_list = train_labels_list[:int(len(train_labels_list)*train_set_percentage)]
val_filenames_list = val_filenames_list[int(len(val_filenames_list)*train_set_percentage):]
val_labels_list = val_labels_list[int(len(val_labels_list)*train_set_percentage):]
img_rows, img_cols = 299, 299
def _parse_function(filename, label):
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string)
image_decoded = tf.image.decode_gif(image_string)
image_resized = tf.image.resize_images(image_decoded, [img_rows, img_cols])
label = tf.one_hot(label, num_classes)
return image_resized, label
filenames = tf.constant(train_filenames_list)
# Labels that match the training files - from a list
labels = tf.constant(train_labels_list)
# Same as above but for validation set
val_filenames = tf.constant(val_filenames_list)
val_labels = tf.constant(val_labels_list)
train_dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
train_dataset = train_dataset.map(_parse_function)
train_dataset = train_dataset.repeat(100)
train_dataset = train_dataset.batch(10)
valid_dataset = tf.data.Dataset.from_tensor_slices((val_filenames, val_labels))
valid_dataset = valid_dataset.map(_parse_function)
valid_dataset = valid_dataset.repeat(100)
valid_dataset = valid_dataset.batch(10)
base_model = tf.keras.applications.vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.3)(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
opt = tf.keras.optimizers.Adam(lr=0.001)
model.compile(optimizer=opt, loss='categorical_crossentropy',metrics=['accuracy'])
train_steps = int(372/1) #total trains set / batch_size
val_steps = int(43/1)
epochs = 4
history = model.fit( train_dataset, steps_per_epoch = train_steps,
epochs = epochs,
validation_data = valid_dataset,
validation_steps = val_steps)
请任何建议。我是新手，并且已经考虑过改变输入形状。他们的方法我没有工作，我不确定如何继续。
首先请原谅我可怜的英语。我也是tensorflow的新手。通过检查文档，我发现tf.image.decode_gif将为GIF图像返回一个形状[num_frames，height，width，3]的张量。 train_dataset.batch(10)还会为[num_frames, height, width, 3]添加一个维度，因此输入的形状是5维。但模型只作为4维的输入数组。如果您的图像格式都是jpg类型，则可以删除
image_decoded = tf.image.decode_gif(image_string)
下面两个语句会使第一个语句无用，因为这两个语句具有相同的左侧，后者将覆盖第一个语句
image_decoded = tf.image.decode_jpeg(image_string)
image_decoded = tf.image.decode_gif(image_string)