59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
from pathlib import Path
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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# Load the trained model
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model = keras.models.load_model('Network/trained_model.h5')
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# Load the class names
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class_names = ['table', 'done', 'order']
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# Load and preprocess the validation dataset
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data_dir = "Network/Training/"
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image_size = (100, 100)
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batch_size = 32
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val_ds = tf.keras.preprocessing.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=image_size,
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batch_size=batch_size,
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)
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# Select 20 random images from the validation set
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val_images = []
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val_labels = []
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for images, labels in val_ds.unbatch().shuffle(1000).take(60):
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val_images.append(images)
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val_labels.append(labels)
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# Make predictions on the random images
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errorcount = 0
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for i in range(60):
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test_image = val_images[i]
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test_label = val_labels[i]
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test_image = np.expand_dims(test_image, axis=0)
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test_image = test_image / 255.0 # Normalize the image
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# Make predictions
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predictions = model.predict(test_image)
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predicted_class_index = np.argmax(predictions[0])
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predicted_class = class_names[predicted_class_index]
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true_class = class_names[test_label]
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direct = 'Network/Results/'
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filename = predicted_class + str(i) + '.jpeg'
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Path(direct).mkdir(parents=True, exist_ok=True)
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tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
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if predicted_class != true_class:
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errorcount += 1
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print('Image', i+1)
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print('True class:', true_class)
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print('Predicted class:', predicted_class)
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print()
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print('Error count: ', errorcount)
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