45 lines
1.0 KiB
Python
45 lines
1.0 KiB
Python
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import tensorflow as tf
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from keras import layers
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from keras.models import Sequential
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from keras.optimizers import Adam
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from keras.utils import to_categorical
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from keras.preprocessing.image import ImageDataGenerator
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import os
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import PIL
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import PIL.Image
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import numpy
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# Set the paths to the folders containing the training data
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train_data_dir = "Training/"
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validation_data_dir = "Validation/"
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# Set the number of classes and batch size
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num_classes = 3
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batch_size = 32
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# Set the image size and input shape
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img_width, img_height = 100, 100
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input_shape = (img_width, img_height, 3)
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train_ds = tf.keras.utils.image_dataset_from_directory(
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train_data_dir,
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validation_split=0.2,
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subset="training",
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shuffle=True,
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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val_ds = tf.keras.utils.image_dataset_from_directory(
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train_data_dir,
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validation_split=0.2,
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subset="validation",
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shuffle=True,
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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class_names = train_ds.class_names
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print(class_names)
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