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

import pathlib train_dir = "Train/" test_dir = "Test/" train_dir = pathlib.Path(train_dir) test_dir = pathlib.Path(test_dir)

train_image_count = len(list(train_dir.glob('*/*.jpg'))) print(train_image_count)

test_image_count = len(list(test_dir.glob('*/*.jpg'))) print(test_image_count)

Rhino = list(train_dir.glob('Rhino/*')) PIL.Image.open(str(Rhino[0]))

PIL.Image.open(str(Rhino[1]))

batch_size = 32 img_height = 256 img_width = 256

train_ds = tf.keras.utils.image_dataset_from_directory( train_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( test_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)

import matplotlib.pyplot as plt 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()

data_augmentation = keras.Sequential( [ layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)), layers.RandomRotation(0.1), layers.RandomZoom(0.1), ] )

plt.figure(figsize=(10, 10)) for images, _ in train_ds.take(1): for i in range(9): augmented_images = data_augmentation(images) ax = plt.subplot(3, 3, i + 1) plt.imshow(augmented_images[0].numpy().astype("uint8")) plt.axis("off")

model = Sequential([ data_augmentation, layers.Rescaling(1./255), 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.Dropout(0.2), 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 = 15 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()