Pre-processing of Data

RockPaperScissors.py

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+import tensorflow as tf
+import tensorflow_datasets as tfds
+import matplotlib.pyplot as plt
+import numpy as np
+import platform
+import datetime
+import os
+import math
+import random
+
+DATASET_NAME = 'rock_paper_scissors'
+
+(dataset_train_raw, dataset_test_raw), dataset_info = tfds.load(
+    name=DATASET_NAME,
+    data_dir='tmp',
+    with_info=True,
+    as_supervised=True,
+    split=[tfds.Split.TRAIN, tfds.Split.TEST],
+)
+
+#print(dataset_train_raw)
+
+print('Raw train dataset:', dataset_train_raw)
+print('Raw train dataset size:', len(list(dataset_train_raw)), '\n')
+
+print('Raw test dataset:', dataset_test_raw)
+print('Raw test dataset size:', len(list(dataset_test_raw)), '\n')
+
+NUM_TRAIN_EXAMPLES = dataset_info.splits['train'].num_examples
+NUM_TEST_EXAMPLES = dataset_info.splits['test'].num_examples
+NUM_CLASSES = dataset_info.features['label'].num_classes
+
+print('Number of TRAIN examples:', NUM_TRAIN_EXAMPLES)
+print('Number of TEST examples:', NUM_TEST_EXAMPLES)
+print('Number of label classes:', NUM_CLASSES)
+
+INPUT_IMG_SIZE_ORIGINAL = dataset_info.features['image'].shape[0]
+INPUT_IMG_SHAPE_ORIGINAL = dataset_info.features['image'].shape
+
+INPUT_IMG_SIZE_REDUCED = INPUT_IMG_SIZE_ORIGINAL // 2
+INPUT_IMG_SHAPE_REDUCED = (
+    INPUT_IMG_SIZE_REDUCED,
+    INPUT_IMG_SIZE_REDUCED,
+    INPUT_IMG_SHAPE_ORIGINAL[2]
+)
+
+# Here we may switch between bigger or smaller image sized that we will train our model on.
+INPUT_IMG_SIZE = INPUT_IMG_SIZE_REDUCED
+INPUT_IMG_SHAPE = INPUT_IMG_SHAPE_REDUCED
+
+print('Input image size (original):', INPUT_IMG_SIZE_ORIGINAL)
+print('Input image shape (original):', INPUT_IMG_SHAPE_ORIGINAL)
+print('\n')
+print('Input image size (reduced):', INPUT_IMG_SIZE_REDUCED)
+print('Input image shape (reduced):', INPUT_IMG_SHAPE_REDUCED)
+print('\n')
+print('Input image size:', INPUT_IMG_SIZE)
+print('Input image shape:', INPUT_IMG_SHAPE)
+
+# Function to convert label ID to labels string.
+get_label_name = dataset_info.features['label'].int2str
+
+print(get_label_name(0));
+print(get_label_name(1));
+print(get_label_name(2));
+
+
+def preview_dataset(dataset):
+    plt.figure(figsize=(12, 12))
+    plot_index = 0
+    for features in dataset.take(12):
+        (image, label) = features
+        plot_index += 1
+        plt.subplot(3, 4, plot_index)
+        # plt.axis('Off')
+        label = get_label_name(label.numpy())
+        plt.title('Label: %s' % label)
+        plt.imshow(image.numpy())
+        plt.show()
+
+# Explore raw training dataset images.
+preview_dataset(dataset_train_raw)
+
+
+