二进制输出的模型和分类报告之间的 Keras 准确性不同

这是我如何加载包含图像数据的 2 个文件夹的数据：

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    main_folder,
    validation_split=0.1,
    subset="training",
    seed=123,
    image_size=(dim, dim))

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    main_folder,
    validation_split=0.1,
    subset="validation",
    seed=123,
    image_size=(dim, dim))

从文件夹加载训练数据给出

Found 6457 files belonging to 2 classes.
Using 5812 files for training.
Found 6457 files belonging to 2 classes.
Using 645 files for validation.

这是我训练模型的方法：

model = tf.keras.models.Sequential([
    tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255),
    tf.keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same'),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(loss=tf.losses.BinaryCrossentropy(from_logits=True), optimizer="adam", metrics=["accuracy"])

es = EarlyStopping(monitor='val_accuracy', min_delta=0.1, patience=5)

model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epc,
    callbacks=[es])

这是我得到结果的方式：

y_pred = model.predict(val_ds)
predicted_categories = tf.argmax(y_pred, axis=1)
true_categories = tf.concat([y for x, y in val_ds], axis=0)
    
print(classification_report(true_categories, predicted_categories ))

矛盾的输出是：

Epoch 1/100
182/182 [==============================] - 8s 44ms/step - loss: 0.6617 - accuracy: 0.5139 - val_loss: 0.6466 - val_accuracy: 0.3442
Epoch 2/100
182/182 [==============================] - 8s 46ms/step - loss: 0.6613 - accuracy: 0.5712 - val_loss: 0.6460 - val_accuracy: 0.6558
Epoch 3/100
182/182 [==============================] - 8s 44ms/step - loss: 0.6611 - accuracy: 0.5594 - val_loss: 0.6474 - val_accuracy: 0.3442
Epoch 4/100
182/182 [==============================] - 8s 46ms/step - loss: 0.6315 - accuracy: 0.6504 - val_loss: 0.4623 - val_accuracy: 0.9690
Epoch 5/100
182/182 [==============================] - 8s 46ms/step - loss: 0.4780 - accuracy: 0.9554 - val_loss: 0.4597 - val_accuracy: 0.9690
Epoch 6/100
182/182 [==============================] - 8s 45ms/step - loss: 0.4831 - accuracy: 0.9434 - val_loss: 0.4517 - val_accuracy: 0.9845
Epoch 7/100
182/182 [==============================] - 8s 45ms/step - loss: 0.4720 - accuracy: 0.9658 - val_loss: 0.4546 - val_accuracy: 0.9736
Epoch 8/100
182/182 [==============================] - 8s 44ms/step - loss: 0.4719 - accuracy: 0.9652 - val_loss: 0.4507 - val_accuracy: 0.9860
Epoch 9/100
182/182 [==============================] - 8s 44ms/step - loss: 0.4747 - accuracy: 0.9597 - val_loss: 0.4528 - val_accuracy: 0.9814

              precision    recall  f1-score   support
           0       0.34      1.00      0.51       222
           1       0.00      0.00      0.00       423
    accuracy                           0.34       645
   macro avg       0.17      0.50      0.26       645
weighted avg       0.12      0.34      0.18       645

否则，每次执行时我都会得到不同的答案

有人可以请教为什么分类报告的准确率为 34% 而模型 val_accuracy 为 0.94%？