plt.imshow(train_set_x_orig[index])
plt.show()
print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") + "' picture.")
'''
# Flatten the images
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
# Normalise image values
train_set_x = train_set_x_flatten / 255.
test_set_x = test_set_x_flatten / 255.
# Create model instance
model = LogisticRegression()
# Fit model to the data
model.fit(train_set_x, train_set_y)
# Train the model
model.train(2400, verbose=True)
# Predict values
predictions = model.predict(test_set_x)
# Check accuracy
model.print_accuracy(predictions, test_set_y)
# Plot training loss
model.plot_cost()
