Heiko J Schick
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
import keras
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from keras.models import Model, load_model
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from keras.preprocessing.image import ImageDataGenerator
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import matplotlib.pyplot as plt
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import numpy as np
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train_datagen = ImageDataGenerator(validation_split=0.3,
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shear_range=0.2,
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zoom_range=0.2,
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horizontal_flip=True)
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train_path = 'images/train/'
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test_path = 'images/test/'
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batch_size = 16
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image_size = 224
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num_class = 8
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### Testing our model
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model = load_model('fine_tune.h5')
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test_datagen = ImageDataGenerator()
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train_generator = train_datagen.flow_from_directory(
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directory=train_path,
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target_size=(image_size,image_size),
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batch_size=batch_size,
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class_mode='categorical',
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color_mode='rgb',
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shuffle=True)
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test_generator = test_datagen.flow_from_directory(
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directory=test_path,
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target_size=(image_size, image_size),
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color_mode='rgb',
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shuffle=False,
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class_mode='categorical',
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batch_size=1)
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filenames = test_generator.filenames
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nb_samples = len(filenames)
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fig=plt.figure()
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columns = 4
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rows = 4
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for i in range(1, columns*rows -1):
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x_batch, y_batch = test_generator.next()
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name = model.predict(x_batch)
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name = np.argmax(name, axis=-1)
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true_name = y_batch
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true_name = np.argmax(true_name, axis=-1)
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label_map = (test_generator.class_indices)
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label_map = dict((v,k) for k,v in label_map.items()) #flip k,v
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predictions = [label_map[k] for k in name]
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true_value = [label_map[k] for k in true_name]
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image = x_batch[0].astype(np.int)
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fig.add_subplot(rows, columns, i)
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plt.title(str(predictions[0]) + ':' + str(true_value[0]))
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plt.imshow(image)
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plt.show()
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'''
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And our test is as given below! Only 1 image is predicted wrong from a test of
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14 images!
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''' |