history = model.fit(x=X_train,
y=y_train,
verbose=2,
epochs=3,
batch_size=32,
validation_split=0.1,
callbacks=[early_stopping, save_model])
digit_test = pd.read_csv(os.path.join(path, "test.csv"))
digit_test.shape
digit_test.info()
X_test = digit_test.values.astype('float32') / 255.0
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
digit_test['Label'] = np.argmax(model.predict(X_test), axis=1)
digit_test['ImageId'] = list(range(1, X_test.shape[0] + 1))
digit_test.to_csv(os.path.join(path, "submission.csv"),
index=False,
columns=['ImageId', 'Label'])
index = digit_test[digit_test.Label == 5]
print(index.head())
act = kutils.get_activations(model, X_test[23:24])
kutils.display_activations(act,
directory=os.path.join(path, 'digit_activations'),
save=True)
kutils.display_heatmaps(act,
X_test_images[0:1],
directory=os.path.join(path, 'digit_heatmaps'),
save=True)
callbacks=[save_weights, early_stopping])
kutils.plot_loss_accuracy(history)
test_datagen = ImageDataGenerator(rescale=1. / 255)
test_generator = test_datagen.flow_from_directory(test_dir,
target_size=(img_width,
img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
tmp = next(test_generator)
act = kutils.get_activations(model, tmp[0:1]) # with just one sample.
kutils.display_activations(act,
directory=os.path.join("D:/cats vs dogs",
'digit_activations'),
save=True)
kutils.display_heatmaps(act,
tmp[0:1],
directory=os.path.join("D:/cats vs dogs",
'digit_heatmaps'),
save=True)
#print(test_generator.filenames)
probabilities = model.predict_generator(test_generator,
nb_test_samples // batch_size)
mapper = {}
i = 0
for file in test_generator.filenames:
id = int(file.split('\\')[1].split('.')[0])