67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
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import tensorflow as tf
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import numpy as np
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from tensorflow.keras import layers
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from tensorflow.keras.models import Sequential
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import pathlib
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data_dir = pathlib.Path("C:/Users/Natalia/Documents/smieci")
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batch_size = 32
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img_height = 180
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img_width = 180
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train_ds = tf.keras.preprocessing.image_dataset_from_directory(
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data_dir,
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validation_split=0.2,
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subset="training",
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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.preprocessing.image_dataset_from_directory(
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data_dir,
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validation_split=0.2,
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subset="validation",
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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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AUTOTUNE = tf.data.AUTOTUNE
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train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
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val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
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normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
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normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
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image_batch, labels_batch = next(iter(normalized_ds))
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first_image = image_batch[0]
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# Notice the pixels values are now in `[0,1]`.
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print(np.min(first_image), np.max(first_image))
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num_classes = 4
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model = Sequential([
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layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
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layers.Conv2D(16, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(32, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(64, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Flatten(),
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layers.Dense(128, activation='relu'),
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layers.Dense(num_classes)
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])
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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epochs=10
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history = model.fit(
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train_ds,
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validation_data=val_ds,
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epochs=epochs
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)
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model.save('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model_vers2')
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