import matplotlib.pyplot as plt import numpy as np import os import PIL import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.models import Sequential batch_size = 32 img_height = 180 img_width = 180 data_dir = "../SI_InteligentnyWozekWidlowy/imageClasification/images" train_ds = tf.keras.utils.image_dataset_from_directory( data_dir, validation_split=0.2, subset="training", seed=123, image_size=(img_height, img_width), batch_size=batch_size) val_ds = tf.keras.utils.image_dataset_from_directory( data_dir, validation_split=0.2, subset="validation", seed=123, image_size=(img_height, img_width), batch_size=batch_size) class_names = train_ds.class_names print(class_names) plt.figure(figsize=(10, 10)) for images, labels in train_ds.take(1): for i in range(9): ax = plt.subplot(3, 3, i + 1) plt.imshow(images[i].numpy().astype("uint8")) plt.title(class_names[labels[i]]) plt.axis("off") for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break AUTOTUNE = tf.data.AUTOTUNE train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE) normalization_layer = layers.Rescaling(1. / 255) normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y)) image_batch, labels_batch = next(iter(normalized_ds)) first_image = image_batch[0] # Notice the pixel values are now in `[0,1]`. print(np.min(first_image), np.max(first_image)) num_classes = len(class_names) model = Sequential([ layers.Rescaling(1. / 255, input_shape=(img_height, img_width, 3)), layers.Conv2D(16, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Conv2D(32, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Conv2D(64, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(num_classes) ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) # model.summary() epochs = 10 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs ) acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) plt.figure(figsize=(8, 8)) plt.subplot(1, 2, 1) plt.plot(epochs_range, acc, label='Training Accuracy') plt.plot(epochs_range, val_acc, label='Validation Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.subplot(1, 2, 2) plt.plot(epochs_range, loss, label='Training Loss') plt.plot(epochs_range, val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show() # ścieżka do sprawdzanego obrazu image_path = "./th-367101945.jpg" img = tf.keras.utils.load_img( image_path, target_size=(img_height, img_width) ) img_array = tf.keras.utils.img_to_array(img) img_array = tf.expand_dims(img_array, 0) # Create a batch predictions = model.predict(img_array) score = tf.nn.softmax(predictions[0]) print( "This image most likely belongs to {} with a {:.2f} percent confidence." .format(class_names[np.argmax(score)], 100 * np.max(score)) )