init_mut_std = 1e-3
init_cross_p = 0.7
model = Model()
model.add(Convolutional(num_filters, filter_size, filter_size, pad=pad))
model.add(ActivationLayer(Activations.tanh))
model.add(MaxPooling(pool_size, pool_size))
model.add(PReLULayer())
model.add(Dropout(dropout_p))
model.add(FullyConnected(bias_initializer=Initializers.glorot_uniform))
model.add(ActivationLayer(Activations.alt_softmax))
model.compile(X_train, y_train)
X_batches, y_batches, num_batches = \
ok.make_batches(X_train, y_train, batch_size)
def initialize(population_size):
population = []
for i in range(population_size):
model.randomize_params(X_train, y_train)
individual = {'genome': model.get_params_as_vec()}
individual['genome'] = np.append(individual['genome'], [init_mut_std, init_mut_p, init_cross_p])
population.append(individual)
return population
def evaluate(population):
for individual in population:
model.set_params_as_vec(individual['genome'][:-3])
tree.add_layer(PReLULayer())
tree.add_layer(FullyConnected())
tree.add_layer(ActivationLayer(Activations.tanh))
tree.add_input(X_reward[0])
tree.add_input(X_reward[1])
model = Model()
model.set_tree(tree)
model.set_loss(Losses.MeanSquared())
learning_rate = 0.00002
model.set_optimizer(Optimizers.RMSprop(learning_rate=learning_rate))
# model.compile(X_reward, y_reward)
# model.train(X_reward, y_reward, 24)
reinforce_index = X_obs.shape[0]
X_batches, y_batches, num_batches = ok.make_batches([X_train], y_train, batch_size=10000)
for i in range(8):
print('\n---Iteration {}---'.format(i + 1))
for X_batch, y_batch in zip(X_batches, y_batches):
model.train(X_reward, y_reward, 24)
accuracy, preds = model.get_dream_accuracy([X_batch[0], None], y_batch)
preds = preds[0]
preds += Initializers.normal(preds.shape, 0.01)
print('Accuracy: {}%'.format(accuracy))
preds_reward = reward(preds.reshape(preds.shape[0], preds.shape[1]).astype('float32'),
y_batch.reshape(preds.shape[0], preds.shape[1]).astype('float32'))
print('Avg Reward: {}'.format(np.mean(preds_reward)))
X_reward[0] = np.append(X_reward[0], X_batch[0], axis=0)
X_reward[1] = np.append(X_reward[1], preds, axis=0)
y_reward = np.append(y_reward, preds_reward.reshape(preds_reward.shape[0], 1), axis=0)
params = model.get_params_as_vec()
