38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
import os
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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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train_data_dir = "Training/"
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train_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir, validation_split=0.2,
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subset="training", seed=123, batch_size=32, image_size=(100, 100))
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val_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir, validation_split=0.2,
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subset="validation", seed=123, batch_size=32, image_size=(100, 100))
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model = keras.models.load_model("trained_model")
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predictions = model.predict(val_ds.take(32))
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classNames = ['Empty', 'Food','People']
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# Make predictions
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direct = ''
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i = 0
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for image, _ in val_ds.take(32):
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predicted_class_index = np.argmax(predictions[i])
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predicted_class = classNames[predicted_class_index]
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filename = predicted_class + str(i) + '.jpeg'
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tf.keras.preprocessing.image.save_img(direct+filename, image[0])
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print('Predicted class:', predicted_class)
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i += 1
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#direct = ''
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#i = 0
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#for image, _ in val_ds.take(32):
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# predictedLabel = int(predictions[i] >= 0.5)
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#
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# filename = classNames[predictedLabel] + str(i) + '.jpeg'
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# tf.keras.preprocessing.image.save_img(direct+filename, image[0])
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# i += 1 |