def rounds(self):
if self.current_file_index >= len(self.files):
return
# print("===> Now Operate on File: " + str(self.files[self.current_file_index]))
self.round_no += 1
train_data = load_data(self.files[self.current_file_index])
self.current_file_index += 1
ROUND_BATCH = int(len(train_data) * 0.1)
DATA_LENGTH = len(train_data)
next_line = 0
while next_line < DATA_LENGTH:
self.process_one_round(train_data, next_line, ROUND_BATCH)
next_line += ROUND_BATCH
self.merge_CS()
self.make_CS()
# self.merge_CS() # Change to Merge First Than Make
self.information_summary()
displaySet(self.DS_set)
self.rounds() # Trigger next round
def initialisation(self):
# print("===> Now Operate on File: " + str(self.files[self.current_file_index]))
self.round_no += 1
train_data = load_data(self.files[self.current_file_index])
self.current_file_index += 1
train_data, sample = sample_data(train_data, self.sample_ratio,
self.beta)
ROUND_BATCH = int(len(train_data) * 0.1)
DATA_LENGTH = len(train_data)
X = sample
keamns = _KMeans(K=self.K, tol=1, compulsory=True, n_init=15)
Y = keamns.fit(X)
self.DS_set = initialise_DS(X, Y, self.K)
# print("**** Initialisation Begin ****")
displaySet(self.DS_set)
next_line = 0
while next_line < DATA_LENGTH:
self.process_one_round(train_data, next_line, ROUND_BATCH)
next_line += ROUND_BATCH # Save
self.make_CS()
self.information_summary()
y -- true labels
p -- predictions
"""
a = p + y
mislabeled_indices = np.asarray(np.where(a == 1))
plt.rcParams['figure.figsize'] = (40.0, 40.0) # set default size of plots
num_images = len(mislabeled_indices[0])
for i in range(num_images):
index = mislabeled_indices[1][i]
plt.subplot(2, num_images, i + 1)
plt.imshow(X[:,index].reshape(64,64,3), interpolation='nearest')
plt.axis('off')
plt.title("Prediction: " + classes[int(p[0,index])].decode("utf-8") + " \n Class: " + classes[y[0,index]].decode("utf-8"))
train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
# Reshape the training and test examples
train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T # The "-1" makes reshape flatten the remaining dimensions
test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
# Standardize data to have feature values between 0 and 1.
train_x = train_x_flatten/255.
test_x = test_x_flatten/255.
layers_dims = [12288, 7, 7, 5, 1]
parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 5000, print_cost = True, model_name = 'model_v1', save = True)
predictions_train = predict(train_x, train_y, parameters)
def load_all_data(paths):
train_data = []
for input_file_path in paths:
train_data = train_data + load_data(input_file_path)
return train_data