def test_convnet():
batch_size = 500 # size of the minibatch
learning_rate = 0.1
n_epochs = 200
# Load the dataset
f = gzip.open('data/mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
test_set_x, test_set_y = test_set
valid_set_x, valid_set_y = valid_set
train_set_x, train_set_y = train_set
train_set_x = np.asarray(train_set_x, dtype=theano.config.floatX)
train_set_y = np.asarray(train_set_y, dtype=theano.config.floatX)
valid_set_x = np.asarray(valid_set_x, dtype=theano.config.floatX)
valid_set_y = np.asarray(valid_set_y, dtype=theano.config.floatX)
test_set_x = np.asarray(test_set_x, dtype=theano.config.floatX)
test_set_y = np.asarray(test_set_y, dtype=theano.config.floatX)
train_set_x = train_set_x.reshape((train_set_x.shape[0], 1, 28, 28))
test_set_x = test_set_x.reshape((test_set_x.shape[0], 1, 28, 28))
valid_set_x = valid_set_x.reshape((valid_set_x.shape[0], 1, 28, 28))
nn_layers = []
nkerns = [20, 50]
# nn_layers.append(layers.Input2DLayer(batch_size, 1, 28, 28, scale=255))
# nn_layers.append(layers.Conv2DLayer(nn_layers[-1], nkerns[0], 5, 5, .01, .01))
# nn_layers.append(layers.Pooling2DLayer(nn_layers[-1], pool_size=(2, 2)))
# nn_layers.append(layers.Conv2DLayer(nn_layers[-1], nkerns[1], 5, 5, .01, .01))
# nn_layers.append(layers.Pooling2DLayer(nn_layers[-1], pool_size=(2, 2)))
# #nn_layers.append(layers.FlattenLayer(nn_layers[-1]))
# nn_layers.append(layers.DenseLayer(nn_layers[-1], 500, 0.1, 0, nonlinearity=layers.tanh))
# nn_layers.append(layers.SoftmaxLayer(nn_layers[-1], 10, 0.1, 0, nonlinearity=layers.tanh))
# #nn_layers.append(layers.OutputLayer(nn_layers[-1]))
nn_layers.append(layers.Input2DLayer(batch_size, 1, 28, 28))
nn_layers.append(
layers.StridedConv2DLayer(nn_layers[-1],
n_filters=nkerns[0],
filter_width=5,
filter_height=5,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01,
nonlinearity=T.tanh))
nn_layers.append(
layers.StridedConv2DLayer(nn_layers[-1],
n_filters=nkerns[1],
filter_width=5,
filter_height=5,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01,
nonlinearity=T.tanh))
nn_layers.append(
layers.DenseLayer(nn_layers[-1], 500, 0.1, 0,
nonlinearity=layers.tanh))
nn_layers.append(
layers.SoftmaxLayer(nn_layers[-1],
10,
0.1,
0,
nonlinearity=layers.tanh))
mlp = NN(nn_layers,
learning_rate=learning_rate,
batch_size=batch_size,
discrete_target=True)
mlp.train_model_batch_patience(train_set_x,
train_set_y,
valid_set_x,
valid_set_y,
test_set_x,
test_set_y,
n_epochs=n_epochs)
# start_time = time.clock()
# train_losses = mlp.train_model_batch(train_set_x, train_set_y, n_epochs)
# end_time = time.clock()
# print >> sys.stderr, ('The code ran for %.2fm' % ((end_time - start_time) / 60.))
# print 'train losses'
# print train_losses
# print 'mean train loss'
# np.mean(train_losses)
# print 'testing'
# #test_mb_size = test_set_x.shape[0]
# #nn_layers[0].mb_size = test_mb_size
# #mlp_test = NN(nn_layers, batch_size=test_mb_size)
# predicted_classes = mlp.output_model_batch(test_set_x)
# miss = predicted_classes != test_set_y
# test_error_rate = float(len(miss[miss])) / len(miss)
# print test_error_rate
print 'done'
num_valid = num_train // 10 # integer division
num_train -= num_valid
num_test = load_data.num_test
valid_ids = load_data.train_ids[num_train:]
train_ids = load_data.train_ids[:num_train]
test_ids = load_data.test_ids
train_indices = np.arange(num_train)
valid_indices = np.arange(num_train, num_train + num_valid)
test_indices = np.arange(num_test)
y_valid = np.load("data/solutions_train.npy")[num_train:]
print("Build model")
l0 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[0][0],
input_sizes[0][1])
l0_45 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[1][0],
input_sizes[1][1])
l0r = layers.MultiRotSliceLayer([l0, l0_45], part_size=45, include_flip=True)
l0s = cc_layers.ShuffleBC01ToC01BLayer(l0r)
l1a = cc_layers.CudaConvnetConv2DLayer(l0s,
n_filters=32,
filter_size=6,
weights_std=0.01,
init_bias_value=0.1,
dropout=0.0,
partial_sum=1,
untie_biases=True)
def __init__(self,
num_actions,
phi_length,
width,
height,
discount=.9,
learning_rate=.01,
batch_size=32,
approximator='none'):
self._batch_size = batch_size
self._num_input_features = phi_length
self._phi_length = phi_length
self._img_width = width
self._img_height = height
self._discount = discount
self.num_actions = num_actions
self.learning_rate = learning_rate
self.scale_input_by = 255.0
print "neural net initialization, lr is: ", self.learning_rate, approximator
# CONSTRUCT THE LAYERS
self.q_layers = []
self.q_layers.append(
layers.Input2DLayer(self._batch_size, self._num_input_features,
self._img_height, self._img_width,
self.scale_input_by))
if approximator == 'cuda_conv':
self.q_layers.append(
cc_layers.ShuffleBC01ToC01BLayer(self.q_layers[-1]))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_size=8,
stride=4,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_size=4,
stride=2,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.ShuffleC01BToBC01Layer(self.q_layers[-1]))
elif approximator == 'conv':
self.q_layers.append(
layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_width=8,
filter_height=8,
stride_x=4,
stride_y=4,
weights_std=.01,
init_bias_value=0.01))
self.q_layers.append(
layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_width=4,
filter_height=4,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01))
if approximator == 'cuda_conv' or approximator == 'conv':
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=256,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.rectify))
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.identity))
if approximator == 'none':
self.q_layers.append(\
layers.DenseLayerNoBias(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.00,
dropout=0,
nonlinearity=layers.identity))
self.q_layers.append(layers.OutputLayer(self.q_layers[-1]))
for i in range(len(self.q_layers) - 1):
print self.q_layers[i].get_output_shape()
# Now create a network (using the same weights)
# for next state q values
self.next_layers = copy_layers(self.q_layers)
self.next_layers[0] = layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_width,
self._img_height,
self.scale_input_by)
self.next_layers[1].input_layer = self.next_layers[0]
self.rewards = T.col()
self.actions = T.icol()
# Build the loss function ...
print "building loss funtion"
q_vals = self.q_layers[-1].predictions()
next_q_vals = self.next_layers[-1].predictions()
next_maxes = T.max(next_q_vals, axis=1, keepdims=True)
target = self.rewards + discount * next_maxes
target = theano.gradient.consider_constant(target)
diff = target - q_vals
# Zero out all entries for actions that were not chosen...
mask = build_mask(T.zeros_like(diff), self.actions, 1.0)
diff_masked = diff * mask
error = T.mean(diff_masked**2)
self._loss = error * diff_masked.shape[1] #
self._parameters = layers.all_parameters(self.q_layers[-1])
self._idx = T.lscalar('idx')
# CREATE VARIABLES FOR INPUT AND OUTPUT
self.states_shared = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.states_shared_next = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.rewards_shared = theano.shared(np.zeros(
(1, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(np.zeros((1, 1), dtype='int32'),
broadcastable=(False, True))
self._givens = \
{self.q_layers[0].input_var:
self.states_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.next_layers[0].input_var:
self.states_shared_next[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.rewards:
self.rewards_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :],
self.actions:
self.actions_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :]
}
self._updates = layers.gen_updates_rmsprop_and_nesterov_momentum(\
self._loss, self._parameters, learning_rate=self.learning_rate,
rho=0.9, momentum=0.9, epsilon=1e-6)
self._train = theano.function([self._idx],
self._loss,
givens=self._givens,
updates=self._updates)
self._compute_loss = theano.function([self._idx],
self._loss,
givens=self._givens)
self._compute_q_vals = \
theano.function([self.q_layers[0].input_var],
self.q_layers[-1].predictions(),
on_unused_input='ignore')