Image data augmentation and training - conv-neural-network

Question

I am using Cats Vs Dogs dataset which contains 2000 images in 2 categories and divided into train and validation directory which can be download here.
I am trying to use real-time image augmentation to feed into a CNN model using train and validation generators. I am using Python 3.8 and TF2.5. The code is as follows:
path_to_imgs = "cats_and_dogs_filtered\\"
# Define the train and validation directory-
train_dir = os.path.join(path_to_imgs, 'train')
val_dir = os.path.join(path_to_imgs, 'validation')
batch_size = 64
IMG_HEIGHT, IMG_WIDTH = 150, 150
def plotImages(images_arr):
# function to plot 5 images together-
fig, axes = plt.subplots(1, 5, figsize=(20,20))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(img)
ax.axis('off')
plt.tight_layout()
plt.show()
return None
# Use image augmentation for training dataset-
image_generator = ImageDataGenerator(
rescale = 1./255, rotation_range = 135)
train_data_gen = image_generator.flow_from_directory(
directory = train_dir, batch_size = batch_size,
shuffle = True, target_size = (IMG_HEIGHT, IMG_WIDTH),
class_mode = 'sparse'
)
# Found 2000 images belonging to 2 classes.
# Validation images need no augmentations-
val_data_gen = tf.keras.preprocessing.image_dataset_from_directory(
val_dir, image_size = (IMG_HEIGHT, IMG_WIDTH),
batch_size = batch_size)
# Found 1000 files belonging to 2 classes.
# Configure the dataset for performance-
# AUTOTUNE = tf.data.AUTOTUNE
# val_data_gen = val_data_gen.cache().prefetch(buffer_size = AUTOTUNE)
val_data_gen = val_data_gen.take(batch_size).cache().repeat()
augmented_images = [train_data_gen[0][0][0] for i in range(5)]
plotImages(augmented_images)
# Get a batch of training images and labels-
x, y = next(iter(train_data_gen))
# Get a batch of validation images and labels-
x_t, y_t = next(iter(val_data_gen))
x.shape, y.shape
# ((64, 150, 150, 3), (64,))
x_t.shape, y_t.shape
# (TensorShape([64, 150, 150, 3]), TensorShape([64]))
weight_decay = 0.0005
model = Sequential()
model.add(
Conv2D(
filters = 64, kernel_size = (3, 3),
activation='relu', kernel_initializer = tf.initializers.he_normal(),
strides = (1, 1), padding = 'same', kernel_regularizer = regularizers.l2(weight_decay),
input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)
)
)
model.add(
Conv2D(
filters = 64, kernel_size = (3, 3),
activation='relu', kernel_initializer = tf.initializers.he_normal(),
strides = (1, 1), padding = 'same', kernel_regularizer = regularizers.l2(weight_decay)
)
)
model.add(
# AveragePooling2D(
MaxPooling2D(
pool_size=(2, 2), strides=(2, 2)
)
)
model.add(
Conv2D(
filters = 128, kernel_size = (3, 3),
activation='relu', kernel_initializer = tf.initializers.he_normal(),
strides = (1, 1), padding = 'same', kernel_regularizer = regularizers.l2(weight_decay)
)
)
model.add(
Conv2D(
filters = 128, kernel_size = (3, 3),
activation='relu', kernel_initializer = tf.initializers.he_normal(),
strides = (1, 1), padding = 'same', kernel_regularizer = regularizers.l2(weight_decay)
)
)
model.add(
# AveragePooling2D(
MaxPooling2D(
pool_size=(2, 2), strides=(2, 2)
)
)
model.add(
Conv2D(
filters = 256, kernel_size = (3, 3),
activation='relu', kernel_initializer = tf.initializers.he_normal(),
strides = (1, 1), padding = 'same', kernel_regularizer = regularizers.l2(weight_decay)
)
)
model.add(
Conv2D(
filters = 256, kernel_size = (3, 3),
activation='relu', kernel_initializer = tf.initializers.he_normal(),
strides = (1, 1), padding = 'same', kernel_regularizer = regularizers.l2(weight_decay)
)
)
model.add(
AveragePooling2D(
# MaxPooling2D(
pool_size=(2, 2), strides=(2, 2)
)
)
model.add(Flatten())
model.add(
Dense(
units = 2, activation = 'sigmoid'
)
)
# Compile defined model-
model.compile(
optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001),
# loss=tf.losses.SparseCategoricalCrossentropy(from_logits = True),
# loss = tf.losses.SparseCategoricalCrossentropy(),
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy']
)
model(x).shape
# TensorShape([64, 2])
model.predict(x).shape
# (64, 2)
'''
# This is deprecated in favor of model.fit()-
model.fit_generator(
generator = train_data_gen, steps_per_epoch = len(train_data_gen),
epochs = 5
)
'''
model.fit(train_data_gen, val_data_gen, batch_size = batch_size, epochs = 5)
Using "model.fit()" gives the error:
ValueError: y argument is not supported when using
keras.utils.Sequence as input.
What am I doing wrong?