InvalidArgumentError: Невозможно сжать dim [1], ожидаемое измерение 1, получено 2 [[{{node metrics_2 / acc / Squeeze}}]]]

Question

Я хотел обучить свой cnn, используя Google Colab, но одна ошибка продолжает появляться в выделенной строке. Ошибка: InvalidArgumentError: Невозможно сжать dim [1], ожидалось измерение 1, получено 2 [[{{node metrics_2 / acc / Squeeze}}]] Ниже приведены мои коды: Могу ли я знать, что я должен сделать, чтобы исправить коды и что вызывает проблему. Это очень важно для того, чтобы я получил высшее образование. Пожалуйста помоги.

    !pip install tensorflow==1.14.0
    !pip install keras==2.2.0
    !pip install tensorflow_hub
    from __future__ import absolute_import, division, print_function, unicode_literals
    import matplotlib.pylab as plt
    from tensorflow import keras


    # Import TensorFlow Datasets

    import tensorflow as tf
    import tensorflow_hub as hub
    import numpy as np
    import pandas as pd
    # Helper libraries
    import math


    import logging
    logger = tf.get_logger()
    logger.setLevel(logging.ERROR)

    !git clone https://github.com/ultralytics/google-images-download

    !pip install google_images_download
    !wget https://chromedriver.storage.googleapis.com/2.42/chromedriver_linux64.zip  && unzip chromedriver_linux64

    !apt-get update # to update ubuntu to correctly run apt install
    !apt install chromium-chromedriver
    !cp /usr/lib/chromium-browser/chromedriver /usr/bin
    import sys
    sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver')
    from selenium import webdriver
    chrome_options = webdriver.ChromeOptions()
    chrome_options.add_argument('--headless')
    chrome_options.add_argument('--no-sandbox')
    chrome_options.add_argument('--disable-dev-shm-usage')
    wd = webdriver.Chrome('chromedriver',chrome_options=chrome_options)
    wd.get("https://www.webite-url.com")

    import os
    #Mount the drive from Google to save the dataset
    from google.colab import drive # this will be our driver
    drive.mount('/gdrive')
    root = '/gdrive/My Drive/'     # if you want to operate on your Google 

    colab_path = '/gdrive/../content/'
    chromedriver_path = '/gdrive/../content/chromedriver'

    chromedriver = chromedriver_path
    %cd /gdrive/../content/google-images-download
    !python bing_scraper.py --search "raw salmon fillet" --limit 80 --download --chromedriver "chromedriver"

    chromedriver = chromedriver_path
    %cd /gdrive/../content/google-images-download
    !python bing_scraper.py --search "raw shrimp" --limit 80 --download --chromedriver "chromedriver"

    dataset_path = '/gdrive/../content/google-images-download/images/raw_salmon_fillet/'
    dataset = [ dataset_path + img_name for img_name in os.listdir( dataset_path ) ]
    dataset[:89]  #Peek the first ten
    %matplotlib inline
    plt.imshow( plt.imread(dataset[49]) )
    plt.grid(b=None) #remove grid

    data_root='/gdrive/../content/google-images-download/images/'

    IMAGE_SHAPE = (224, 224)
    TRAINING_DATA_DIR = str(data_root)
    print(TRAINING_DATA_DIR);
    datagen_kwargs = dict(rescale=1./255, validation_split=.20)
    valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)
    valid_generator = valid_datagen.flow_from_directory(
    TRAINING_DATA_DIR,
    subset="validation",
    shuffle=True,
    target_size=IMAGE_SHAPE
    )
    train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)
    train_generator = train_datagen.flow_from_directory(
    TRAINING_DATA_DIR,
    subset="training",
    shuffle=True,
    target_size=IMAGE_SHAPE)

    image_batch_train, label_batch_train = next(iter(train_generator))
    print("Image batch shape: ", image_batch_train.shape)
    print("Label batch shape: ", label_batch_train.shape)
    dataset_labels = sorted(train_generator.class_indices.items(), key=lambda pair:pair[1])
    dataset_labels = np.array([key.title() for key, value in dataset_labels])
    print(dataset_labels)


    import tensorflow_hub as hub
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(224, 224, 3)),
        tf.keras.layers.MaxPooling2D(2, 2),

        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),

        tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),

        tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),

        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dense(2)
    ])

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])


#place where error occurs
     ***steps_per_epoch = np.ceil(train_generator.samples/train_generator.batch_size)
        val_steps_per_epoch =np.ceil(valid_generator.samples/valid_generator.batch_size)
        EPOCHS = 100
        history = model.fit(
            train_generator,
            steps_per_epoch=int(steps_per_epoch),
            epochs=EPOCHS,
            validation_data=valid_generator,
            validation_steps=int(val_steps_per_epoch)
        )***