129 lines
3.4 KiB
Python
129 lines
3.4 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import os
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import PIL
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.models import Sequential
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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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data_dir = "./images"
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train_ds = tf.keras.utils.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.utils.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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plt.figure(figsize=(10, 10))
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for images, labels in train_ds.take(1):
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for i in range(9):
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ax = plt.subplot(3, 3, i + 1)
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plt.imshow(images[i].numpy().astype("uint8"))
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plt.title(class_names[labels[i]])
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plt.axis("off")
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for image_batch, labels_batch in train_ds:
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print(image_batch.shape)
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print(labels_batch.shape)
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break
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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.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 pixel 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 = len(class_names)
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model = Sequential([
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layers.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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# model.summary()
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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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acc = history.history['accuracy']
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val_acc = history.history['val_accuracy']
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loss = history.history['loss']
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val_loss = history.history['val_loss']
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epochs_range = range(epochs)
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plt.figure(figsize=(8, 8))
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plt.subplot(1, 2, 1)
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plt.plot(epochs_range, acc, label='Training Accuracy')
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plt.plot(epochs_range, val_acc, label='Validation Accuracy')
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plt.legend(loc='lower right')
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plt.title('Training and Validation Accuracy')
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plt.subplot(1, 2, 2)
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plt.plot(epochs_range, loss, label='Training Loss')
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plt.plot(epochs_range, val_loss, label='Validation Loss')
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plt.legend(loc='upper right')
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plt.title('Training and Validation Loss')
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plt.show()
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# ścieżka do sprawdzanego obrazu
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image_path = "./th-367101945.jpg"
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img = tf.keras.utils.load_img(
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image_path, target_size=(img_height, img_width)
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)
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img_array = tf.keras.utils.img_to_array(img)
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img_array = tf.expand_dims(img_array, 0) # Create a batch
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predictions = model.predict(img_array)
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score = tf.nn.softmax(predictions[0])
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print(
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"This image most likely belongs to {} with a {:.2f} percent confidence."
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.format(class_names[np.argmax(score)], 100 * np.max(score))
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)
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