# Import TensorFlow and Other Libraries import os import sys from pathlib import Path import PIL import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from PIL import Image from openvino.runtime import Core from openvino.tools.mo import mo_tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.models import Sequential sys.path.append("../utils") from notebook_utils import download_file # Download and Explore the Dataset import pathlib dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz" data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True) data_dir = pathlib.Path(data_dir) image_count = len(list(data_dir.glob('*/*.jpg'))) print(image_count) roses = list(data_dir.glob('roses/*')) PIL.Image.open(str(roses[0])) PIL.Image.open(str(roses[1])) tulips = list(data_dir.glob('tulips/*')) PIL.Image.open(str(tulips[0])) PIL.Image.open(str(tulips[1])) # Create a Dataset batch_size = 32 img_height = 180 img_width = 180 train_ds = tf.keras.preprocessing.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.preprocessing.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) # Visualize the Data 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 # Configure the Dataset for Performance # AUTOTUNE = tf.data.AUTOTUNE AUTOTUNE = tf.data.experimental.AUTOTUNE train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE) # Standardize the Data normalization_layer = layers.experimental.preprocessing.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 pixels values are now in `[0,1]`. print(np.min(first_image), np.max(first_image)) # Create the Model num_classes = 5 model = Sequential([ layers.experimental.preprocessing.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) ]) # Compile the Model model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
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