使用“keras.utils.Sequence”作为输入时，不支持“y”参数。错误

Question

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Flatten, Dropout, Conv2D, MaxPool2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import EarlyStopping

train_path = "D:\python_scripts\garbage/garbage/"
img_shape = (437, 694, 3)
df = pd.read_csv("mpd.csv")
scaler = MinMaxScaler()
earlyStopping = EarlyStopping(monitor="val_loss", mode="min", patience=2)

y = df[["methane", "plastic", "dsci"]].values

imgGen = ImageDataGenerator(rotation_range=(20), width_shift_range=(
    0.1), height_shift_range=(0.1), zoom_range=(0.2), shear_range=(0.1), fill_mode="nearest")
imgGen.flow_from_directory(train_path)
x = imgGen.flow_from_directory(train_path, class_mode=None,
                               color_mode="rgb", batch_size=16, target_size=(img_shape)[:0])

model = Sequential()

model.add(Conv2D(filters=128, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))
model.add(Conv2D(filters=256, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))
model.add(Conv2D(filters=512, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))
model.add(Conv2D(filters=1024, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))

model.add(Flatten())

model.add(Dense(128, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1024, activation="relu"))
model.add(Dropout(0.5))

model.add(Dense(3))

model.compile(optimizer="adam", loss="mse", metrics=["accuracy"])

model.fit(x=x, y=y, epochs=500, verbose=1, callbacks=[earlyStopping])
model.save("deep.h5")

注意：garbage/garbage/包含图像 mpd.csv是一个CSV文件，对应于garbage/garbage/中的图像

这是输出-

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Flatten, Dropout, Conv2D, MaxPool2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import EarlyStopping

train_path = "D:\python_scripts\garbage/garbage/"
img_shape = (437, 694, 3)
df = pd.read_csv("mpd.csv")
scaler = MinMaxScaler()
earlyStopping = EarlyStopping(monitor="val_loss", mode="min", patience=2)

y = df[["methane", "plastic", "dsci"]].values

imgGen = ImageDataGenerator(rotation_range=(20), width_shift_range=(
    0.1), height_shift_range=(0.1), zoom_range=(0.2), shear_range=(0.1), fill_mode="nearest")
imgGen.flow_from_directory(train_path)
x = imgGen.flow_from_directory(train_path, class_mode=None,
                               color_mode="rgb", batch_size=16, target_size=(img_shape)[:0])

model = Sequential()

model.add(Conv2D(filters=128, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))
model.add(Conv2D(filters=256, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))
model.add(Conv2D(filters=512, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))
model.add(Conv2D(filters=1024, kernel_size=(3, 3),
                 input_shape=img_shape, activation="relu"))
model.add(MaxPool2D(pool_size=(4, 4)))

model.add(Flatten())

model.add(Dense(128, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1024, activation="relu"))
model.add(Dropout(0.5))

model.add(Dense(3))

model.compile(optimizer="adam", loss="mse", metrics=["accuracy"])

model.fit(x=x, y=y, epochs=500, verbose=1, callbacks=[earlyStopping])
model.save("deep.h5")

Answer 1

您使用的数据生成器返回图像及其标签，标签是从目录名称中提取的。如果您有以下目录结构

• 数据
  • 甲烷
    • 图片1.jpg
  • 塑料
    • 图片2.jpg
  • dsci
    • 图片3.jpg

该模型将理解 image1 属于甲烷类，image2 属于塑料类，image3 属于 dsci 类，因此无需传递标签。
如果您没有该目录结构，那么您可能需要根据tf.keras.utils.Sequence类定义自己的生成器类。你可以在这里阅读更多相关内容