def train_network_scenario1(prototypes1, prototypes2, origin, train_data='train_data.p', val_data='validation_data.p'):
"""
Combines the networks for the arm and the eye. Arm is dominant over the eye, as in scenario 2, so the eye recieves its input from the arm and its target.
Also plots a lot of information about loss and accuracy.
Saves the weights of the network
Input: prototypes for the arm, prototypes for the eye, point of origin of both models, trainingdata for the arm, validationdata for the eye
Output: -
"""
epochs = 150 # number of epochs
print 'network1'
network1, train_fn1, val_fn1 = create_network(prototypes1)
print 'network2'
network2, train_fn2, val_fn2 = create_network(prototypes2, n_inputs=4)
print 'Networks done'
eyes = Eyes(origin=origin, visualize=False)
arm = Arm(origin=origin, visualize=False)
print 'moare stuff'
# Arrays for saving performance after each epoch
means_arm = np.zeros(epochs)
stds_arm = np.zeros(epochs)
train_losses_arm = np.zeros(epochs)
val_losses_arm = np.zeros(epochs)
means_eye = np.zeros(epochs)
stds_eye = np.zeros(epochs)
train_losses_eye = np.zeros(epochs)
val_losses_eye = np.zeros(epochs)
dists_eye = np.zeros(epochs)
dists_arm = np.zeros(epochs)
print "Train network"
for e in tqdm(range(epochs)):
total_mean_arm = 0
total_std_arm = 0
total_mean_eye = 0
total_std_eye = 0
total_error_arm = 0
total_error_eye = 0
train_loss_arm = 0
val_loss_arm = 0
train_loss_eye = 0
val_loss_eye = 0
# training epoch
i = 0
for input_batch, output_batch in iterate_data(data_file=train_data):
pred1, train_loss1 = train_fn1(input_batch, output_batch)
arm_angles = np.array([arm.calculate_angles(x, y) for [x, y] in input_batch], dtype='float32') # same targets as arm
eye_positions = [calc_intersect(left, right) for [left, right] in pred1] # get x,y from predicted eye angels
arm_input = np.hstack((input_batch, eye_positions)).astype('float32') # first the eye coordinates, take care when combining prototypes
pred2, train_loss2 = train_fn2(arm_input, arm_angles)
train_loss_arm += train_loss2
train_loss_eye += train_loss1
i += 1
# Take average loss of this epoch
train_loss_arm = train_loss_arm / i
train_loss_eye = train_loss_eye / i
n = 0
# Validation Epoch
for inp_val, out_val in iterate_data(data_file=val_data):
predictions_eye, loss_eye = val_fn1(inp_val, out_val)
dist_eye, mean_eye, std_eye = evaluate(predictions_eye, out_val) # dist_arm is for debugging
arm_angles = np.array([arm.calculate_angles(x, y) for [x, y] in inp_val], dtype='float32')
eye_positions = [calc_intersect(left, right) for [left, right] in predictions_eye]
arm_input = np.hstack((inp_val, eye_positions)).astype('float32')
prediction_arm, loss_arm = val_fn2(arm_input, arm_angles)
dist_arm, mean_arm, std_arm = evaluate(prediction_arm, inp_val)
arm_positions = np.array([arm.move_arm(shoulder, shoulder) for [shoulder, elbow] in prediction_arm])
arm_error_dist, mean_arm_error, std_arm_error = evaluate(arm_positions, inp_val)
eye_error_dist, mean_eye_error, std_eye_error = evaluate(eye_positions, inp_val)
total_error_arm += mean_arm_error
total_error_eye += mean_eye_error
n += 1
total_mean_arm += mean_arm
total_std_arm += std_arm
total_mean_eye += mean_eye
total_std_eye += std_eye
val_loss_arm += loss_arm
val_loss_eye += loss_eye
# Save epoch data
means_arm[e] = total_mean_arm / n
stds_arm[e] = total_std_arm / n
train_losses_arm[e] = train_loss_arm
val_losses_arm[e] = val_loss_arm / n
means_eye[e] = total_mean_eye / n
stds_eye[e] = total_std_eye / n
train_losses_eye[e] = train_loss_eye
val_losses_eye[e] = val_loss_eye / n
dists_eye[e] = total_error_eye / n
dists_arm[e] = total_error_arm / n
# Plots
# Plot mean and std
plt.figure()
meanplot_arm, = plt.plot(means_arm, label='mean arm')
stdplot_arm, = plt.plot(stds_arm, label='std arm')
meanplot_eye, = plt.plot(means_eye, label='mean eye')
stdplot_eye, = plt.plot(stds_eye, label='std eye')
plt.legend(handles=[meanplot_arm, stdplot_arm, meanplot_eye, stdplot_eye])
plt.savefig('../images/scenario1/accuracy_combined.png')
plt.show()
# Plot just the means
plt.figure()
meanplot_arm, = plt.plot(means_arm, label='mean arm')
meanplot_eye, = plt.plot(means_eye, label='mean eye')
plt.legend(handles=[meanplot_arm, meanplot_eye])
plt.savefig('../images/scenario1/accuracy_combined_arm.png')
# Ploot the train and validations losses
plt.figure()
trainplot_arm, = plt.plot(train_losses_arm, label='train loss arm')
valplot_arm, = plt.plot(val_losses_arm, label='val loss arm')
trainplot_eye, = plt.plot(train_losses_eye, label='train loss eye')
valplot_eye, = plt.plot(val_losses_eye, label='val loss eye')
plt.legend(handles=[trainplot_arm, valplot_arm, trainplot_eye, valplot_eye])
plt.savefig('../images/scenario1/loss_combined.png')
plt.show()
# Plot distance errors
plt.figure()
distsplot_arm, = plt.plot(dists_arm, label='Distance Error arm')
plt.legend(handles=[distsplot_arm])
plt.savefig('../images/scenario1/distance_error_arm.png')
plt.show()
np.save('../images/scenario1/distance_arm', dists_arm)
# Plot Distance error ot the eye
plt.figure()
distsplot_eye, = plt.plot(dists_eye, label='Distance Error eye')
plt.legend(handles=[distsplot_eye])
plt.savefig('../images/scenario1/distance_error_eye.png')
plt.show()
np.save('../images/scenario1/distance_eye', dists_eye)
# Save the weights
np.save('network_arm_s1', layers.get_all_param_values(network1))
np.save('network_eye_s1', layers.get_all_param_values(network2))
return # network, predictions
