def __init__(self, depth=9):
self.depth = depth
depth = 9
train = (3, 32, 32)
nfms = [2**(stage + 4) for stage in sorted(list(range(3)) * depth)]
strides = [1 if cur == prev else 2 for cur, prev in zip(nfms[1:], nfms[:-1])]
# Now construct the network
layers = [Conv(**self.conv_params(3, 16))]
layers.append(self.module_s1(nfms[0], True))
for nfm, stride in zip(nfms[1:], strides):
res_module = self.module_s1(nfm) if stride == 1 else self.module_s2(nfm)
layers.append(res_module)
layers.append(BatchNorm())
layers.append(Activation(Rectlin()))
layers.append(Pooling('all', op='avg'))
layers.append(Affine(10, init=Kaiming(local=False),
batch_norm=True, activation=Softmax()))
self.layers = layers
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.initialize(train, cost=cost)
self.model = model
def test_model_get_outputs(backend_default):
(X_train, y_train), (X_test, y_test), nclass = load_mnist()
train_set = DataIterator(X_train[:backend_default.bsz * 3])
init_norm = Gaussian(loc=0.0, scale=0.1)
layers = [Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin()),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
mlp = Model(layers=layers)
out_list = []
mlp.initialize(train_set)
for x, t in train_set:
x = mlp.fprop(x)
out_list.append(x.get().T.copy())
ref_output = np.vstack(out_list)
train_set.reset()
output = mlp.get_outputs(train_set)
assert np.allclose(output, ref_output)
class TreeModel(object):
"""
Container for Tree style test model"
"""
def __init__(self):
self.in_shape = (1, 32, 32)
init_norm = Gaussian(loc=0.0, scale=0.01)
normrelu = dict(init=init_norm, activation=Rectlin())
normsigm = dict(init=init_norm, activation=Logistic(shortcut=True))
normsoft = dict(init=init_norm, activation=Softmax())
# setup model layers
b1 = BranchNode(name="b1")
b2 = BranchNode(name="b2")
p1 = [Affine(nout=100, name="main1", **normrelu),
b1,
Affine(nout=32, name="main2", **normrelu),
Affine(nout=160, name="main3", **normrelu),
b2,
Affine(nout=32, name="main2", **normrelu),
# make next layer big to check sizing
Affine(nout=320, name="main2", **normrelu),
Affine(nout=10, name="main4", **normsoft)]
p2 = [b1,
Affine(nout=16, name="branch1_1", **normrelu),
Affine(nout=10, name="branch1_2", **normsigm)]
p3 = [b2,
Affine(nout=16, name="branch2_1", **normrelu),
Affine(nout=10, name="branch2_2", **normsigm)]
self.cost = Multicost(costs=[GeneralizedCost(costfunc=CrossEntropyMulti()),
GeneralizedCost(costfunc=CrossEntropyBinary()),
GeneralizedCost(costfunc=CrossEntropyBinary())],
weights=[1, 0., 0.])
self.layers = SingleOutputTree([p1, p2, p3], alphas=[1, .2, .2])
self.model = Model(layers=self.layers)
self.model.initialize(self.in_shape, cost=self.cost)
def run(be, fake_dilation, fsz, stride, pad, dilation):
K = 8
strides = stride
padding = pad
be.rng = be.gen_rng(be.rng_seed)
in_shape = 16
while out_shape(in_shape, fsz, stride, dilation, pad) < 3:
in_shape *= 2
train_shape = (1, in_shape, in_shape)
inp = be.array(be.rng.randn(np.prod(train_shape), be.bsz))
init = Gaussian()
layers = [Conv((5, 5, K), init=init),
Conv((fsz, fsz, K), strides=strides, padding=padding, init=init,
dilation=dict(dil_d=1, dil_h=dilation, dil_w=dilation)),
Conv((3, 3, K), init=init),
Affine(nout=1, init=init)]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.initialize(train_shape, cost)
if fake_dilation:
# Perform regular convolution with an expanded filter.
weights = save(model)
new_layers = layers
# Replace the middle layers.
new_fsz = dilated_fsz(fsz, dilation)
new_layers[1] = Conv((new_fsz, new_fsz, K), strides=strides, padding=padding, init=init)
model = Model(layers=new_layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.initialize(train_shape, cost)
load(weights, model, K, fsz, dilation)
print(model)
model.optimizer = GradientDescentMomentum(learning_rate=0.01,
momentum_coef=0.9)
outputs = fprop(model, inp)
weights = bprop(model, outputs)
model.optimizer.optimize(model.layers_to_optimize, epoch=0)
return outputs.get(), weights.get()
def test_conv_rnn(backend_default):
train_shape = (1, 17, 142)
be = NervanaObject.be
inp = be.array(be.rng.randn(np.prod(train_shape), be.bsz))
delta = be.array(be.rng.randn(10, be.bsz))
init_norm = Gaussian(loc=0.0, scale=0.01)
bilstm = DeepBiLSTM(128, init_norm, activation=Rectlin(), gate_activation=Rectlin(),
depth=1, reset_cells=True)
birnn_1 = DeepBiRNN(128, init_norm, activation=Rectlin(),
depth=1, reset_cells=True, batch_norm=False)
birnn_2 = DeepBiRNN(128, init_norm, activation=Rectlin(),
depth=2, reset_cells=True, batch_norm=False)
bibnrnn = DeepBiRNN(128, init_norm, activation=Rectlin(),
depth=1, reset_cells=True, batch_norm=True)
birnnsum = DeepBiRNN(128, init_norm, activation=Rectlin(),
depth=1, reset_cells=True, batch_norm=False, bi_sum=True)
rnn = Recurrent(128, init=init_norm, activation=Rectlin(), reset_cells=True)
lstm = LSTM(128, init_norm, activation=Rectlin(), gate_activation=Rectlin(), reset_cells=True)
gru = GRU(128, init_norm, activation=Rectlin(), gate_activation=Rectlin(), reset_cells=True)
rlayers = [bilstm, birnn_1, birnn_2, bibnrnn, birnnsum, rnn, lstm, gru]
for rl in rlayers:
layers = [
Conv((2, 2, 4), init=init_norm, activation=Rectlin(),
strides=dict(str_h=2, str_w=4)),
Pooling(2, strides=2),
Conv((3, 3, 4), init=init_norm, batch_norm=True, activation=Rectlin(),
strides=dict(str_h=1, str_w=2)),
rl,
RecurrentMean(),
Affine(nout=10, init=init_norm, activation=Rectlin()),
]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.initialize(train_shape, cost)
model.fprop(inp)
model.bprop(delta)
def test_model_get_outputs(backend_default, data):
dataset = MNIST(path=data)
train_set = dataset.train_iter
init_norm = Gaussian(loc=0.0, scale=0.1)
layers = [Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin()),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
mlp = Model(layers=layers)
out_list = []
mlp.initialize(train_set)
for x, t in train_set:
x = mlp.fprop(x)
out_list.append(x.get().T.copy())
ref_output = np.vstack(out_list)
train_set.reset()
output = mlp.get_outputs(train_set)
assert allclose_with_out(output, ref_output[:output.shape[0], :])
# test model benchmark inference
mlp.benchmark(train_set, inference=True, niterations=5)
class MultistreamModel(object):
"""
Container for a multistream test model
"""
def __init__(self):
self.in_shape = [1024, (2538, 38)]
init = Constant(0)
image_path = Sequential([Affine(20, init, bias=init),
Affine(10, init, bias=init)])
sent_path = Sequential([Affine(30, init, bias=init),
Affine(10, init)])
layers = [MergeMultistream(layers=[image_path, sent_path], merge="recurrent"),
Dropout(keep=0.5),
LSTM(4, init, activation=Logistic(), gate_activation=Tanh(), reset_cells=True),
Affine(20, init, bias=init, activation=Softmax())]
self.layers = layers
self.cost = GeneralizedCostMask(CrossEntropyMulti())
self.model = Model(layers=layers)
self.model.initialize(self.in_shape, cost=self.cost)
def __init__(self):
self.in_shape = (3, 32, 32)
relu = Rectlin()
init_use = Constant(0)
conv = dict(init=init_use,
batch_norm=False,
activation=relu)
convp1 = dict(init=init_use,
batch_norm=False,
bias=init_use,
activation=relu,
padding=1)
convp1s2 = dict(init=init_use,
batch_norm=False,
bias=init_use,
padding=1, strides=2)
layers = [Dropout(keep=.8),
Conv((3, 3, 96), **convp1),
Conv((3, 3, 96), **convp1),
Conv((3, 3, 96), **convp1s2),
Dropout(keep=.5),
Conv((3, 3, 192), **convp1),
Conv((3, 3, 192), **convp1),
Conv((3, 3, 192), **convp1s2),
Dropout(keep=.5),
Conv((3, 3, 192), **convp1),
Conv((1, 1, 192), **conv),
Conv((1, 1, 16), **conv),
Pooling(8, op="avg"),
Activation(Softmax())]
self.layers = layers
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.initialize(self.in_shape, cost=cost)
self.model = model
# parse the command line arguments
parser = NeonArgparser(__doc__)
parser.add_argument("hdf5")
parser.add_argument("model_pkl")
args = parser.parse_args()
model = Model(args.model_pkl)
h5s = [h5py.File(args.hdf5)]
num_moves = sum(h['X'].shape[0] for h in h5s)
print("Found {} HDF5 files with {} moves".format(len(h5s), num_moves))
inputs = HDF5Iterator([h['X'] for h in h5s],
[h['y'] for h in h5s],
ndata=(1024 * 1024))
out_predict = h5s[0].require_dataset("predictions", (num_moves, 362), dtype=np.float32)
out_score = h5s[0].require_dataset("scores", (num_moves,), dtype=np.float32)
out_max = h5s[0].require_dataset("best", (num_moves,), dtype=np.float32)
model.initialize(inputs)
for indata, actual, sl in inputs.predict():
prediction = model.fprop(indata, inference=False).get().T
actual = actual.astype(int)
actual_idx = actual[:, 0] * 19 + actual[:, 1]
actual_idx[actual_idx < 0] = 361
out_predict[sl, :] = prediction
out_score[sl] = prediction[range(prediction.shape[0]), actual_idx]
out_max[sl] = prediction.max(axis=1)
print (sl)
nlp = spacy.load('en')
pos_tokens = []
for doc in [nlp(t) for t in input_texts]:
vec = [pos_vocab[t.tag_.lower()] if t.tag_.lower(
) in pos_vocab else len(pos_vocab) + 1 for t in doc]
pos_tokens.append(vec)
pos_features, _ = get_paddedXY_sequence(
pos_tokens, [], sentence_length=sentence_len, shuffle=False)
input_features.append(TaggedTextSequence(
sentence_len, pos_features, vec_input=False))
if len(input_features) > 1:
x = MultiSequenceDataIterator(input_features, ignore_y=True)
else:
x = input_features[0]
model = Model(args.model)
model.initialize(dataset=x)
preds = model.get_outputs(x).argmax(2)
# print inferred tags and original text
print_nps = args.print_only_nps
rev_y = {v + 1: k for k, v in mp['y_vocab'].items()}
for sen, text in zip(preds, input_texts):
text_tokens = text.lower().split()
text_tags = [rev_y[i] for i in sen if i > 0]
if print_nps is True:
print(extract_nps(text_tokens, text_tags))
else:
print(list(zip(text_tokens, text_tags)))
def test_model_serialize(backend_default, data):
dataset = MNIST(path=data)
(X_train, y_train), (X_test, y_test), nclass = dataset.load_data()
train_set = ArrayIterator(
[X_train, X_train], y_train, nclass=nclass, lshape=(1, 28, 28))
init_norm = Gaussian(loc=0.0, scale=0.01)
# initialize model
path1 = Sequential([Conv((5, 5, 16), init=init_norm, bias=Constant(0), activation=Rectlin()),
Pooling(2),
Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())])
path2 = Sequential([Affine(nout=100, init=init_norm, bias=Constant(0), activation=Rectlin()),
Dropout(keep=0.5),
Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())])
layers = [MergeMultistream(layers=[path1, path2], merge="stack"),
Affine(nout=20, init=init_norm, batch_norm=True, activation=Rectlin()),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
tmp_save = 'test_model_serialize_tmp_save.pickle'
mlp = Model(layers=layers)
mlp.optimizer = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
mlp.cost = GeneralizedCost(costfunc=CrossEntropyBinary())
mlp.initialize(train_set, cost=mlp.cost)
n_test = 3
num_epochs = 3
# Train model for num_epochs and n_test batches
for epoch in range(num_epochs):
for i, (x, t) in enumerate(train_set):
x = mlp.fprop(x)
delta = mlp.cost.get_errors(x, t)
mlp.bprop(delta)
mlp.optimizer.optimize(mlp.layers_to_optimize, epoch=epoch)
if i > n_test:
break
# Get expected outputs of n_test batches and states of all layers
outputs_exp = []
pdicts_exp = [l.get_params_serialize() for l in mlp.layers_to_optimize]
for i, (x, t) in enumerate(train_set):
outputs_exp.append(mlp.fprop(x, inference=True))
if i > n_test:
break
# Serialize model
mlp.save_params(tmp_save, keep_states=True)
# Load model
mlp = Model(tmp_save)
mlp.initialize(train_set)
outputs = []
pdicts = [l.get_params_serialize() for l in mlp.layers_to_optimize]
for i, (x, t) in enumerate(train_set):
outputs.append(mlp.fprop(x, inference=True))
if i > n_test:
break
# Check outputs, states, and params are the same
for output, output_exp in zip(outputs, outputs_exp):
assert allclose_with_out(output.get(), output_exp.get())
for pd, pd_exp in zip(pdicts, pdicts_exp):
for s, s_e in zip(pd['states'], pd_exp['states']):
if isinstance(s, list): # this is the batch norm case
for _s, _s_e in zip(s, s_e):
assert allclose_with_out(_s, _s_e)
else:
assert allclose_with_out(s, s_e)
for p, p_e in zip(pd['params'], pd_exp['params']):
assert type(p) == type(p_e)
if isinstance(p, list): # this is the batch norm case
for _p, _p_e in zip(p, p_e):
assert allclose_with_out(_p, _p_e)
elif isinstance(p, np.ndarray):
assert allclose_with_out(p, p_e)
else:
assert p == p_e
os.remove(tmp_save)
class InceptionModel(object):
"""
Container for the inception-like test model
"""
def __init__(self):
self.in_shape = (3, 299, 299)
self.nout = 100
self.pool3s1p1 = dict(fshape=3, padding=1, strides=1, op='avg')
self.pool3s2p0 = dict(fshape=3, strides=2, op='max')
layers = self.main_branch(nout=self.nout)
self.cost = GeneralizedCost(costfunc=CrossEntropyMulti())
self.model = Model(layers=layers)
self.model.initialize(self.in_shape, cost=self.cost)
self.layers = layers
@staticmethod
def conv_params(fsize, nfm, padding='SAME', strides=1,
activation=Rectlin(), batch_norm=True):
fsize = fsize if isinstance(fsize, tuple) else (fsize, fsize)
fshape = fsize + (nfm,)
padding = {'pad_h': (fsize[0] // 2 if padding == 'SAME' else 0),
'pad_w': (fsize[1] // 2 if padding == 'SAME' else 0),
'pad_d': 0}
strides = {'str_h': strides, 'str_w': strides, 'str_d': 1}
return dict(fshape=fshape,
strides=strides,
activation=activation,
padding=padding,
batch_norm=batch_norm,
init=Kaiming(local=True))
def inception(self, kvals, b2fsz=5):
(p1, p2, p3, p4) = kvals
branch1 = [Conv(**self.conv_params(1, p1[0], padding=0, strides=1))]
branch2 = [Conv(**self.conv_params(1, p2[0], padding=0, strides=1)),
Conv(**self.conv_params(b2fsz, p2[1], padding='SAME', strides=1))],
branch3 = [Conv(**self.conv_params(1, p3[0], padding=0, strides=1)),
Conv(**self.conv_params(3, p3[1], padding='SAME', strides=1)),
Conv(**self.conv_params(3, p3[1], padding='SAME', strides=1))]
branch4 = [Pooling(**self.pool3s1p1),
Conv(**self.conv_params(1, p4[0], padding=0, strides=1))]
return MergeBroadcast(layers=[branch1, branch2, branch3, branch4], merge="depth")
def inception_inception(self, kvals):
(p1, p2, p3, p4) = kvals
branch1 = [Conv(**self.conv_params(1, p1[0], padding=0, strides=1))]
i2_branch1 = [Conv(**self.conv_params((1, 3), p2[1], padding='SAME', strides=1))]
i2_branch2 = [Conv(**self.conv_params((3, 1), p2[1], padding='SAME', strides=1))]
branch2 = [Conv(**self.conv_params(1, p2[0], padding=0, strides=1)),
MergeBroadcast(layers=[i2_branch1, i2_branch2], merge="depth")]
i3_branch1 = [Conv(**self.conv_params((1, 3), p3[2], padding='SAME', strides=1))]
i3_branch2 = [Conv(**self.conv_params((3, 1), p3[2], padding='SAME', strides=1))]
branch3 = [Conv(**self.conv_params(1, p3[0], padding=0, strides=1)),
Conv(**self.conv_params(3, p3[1], padding='SAME', strides=1)),
MergeBroadcast(layers=[i3_branch1, i3_branch2], merge="depth")]
branch4 = [Pooling(**self.pool3s1p1),
Conv(**self.conv_params(1, p4[0], padding=0, strides=1))]
return MergeBroadcast(layers=[branch1, branch2, branch3, branch4], merge="depth")
def main_branch(self, nout=100):
return [Conv(**self.conv_params(3, 32, strides=2, padding=0)),
Pooling(**self.pool3s2p0),
Conv(**self.conv_params(1, 80, strides=1, padding=0)),
Pooling(**self.pool3s2p0),
self.inception([(64, ), (48, 64), (64, 96), (32, )], b2fsz=5),
self.inception([(64, ), (48, 64), (64, 96), (64, )], b2fsz=5),
self.inception_inception([(64, ), (64, 64), (64, 64, 64), (192, )]),
self.inception_inception([(64, ), (64, 64), (64, 64, 64), (192, )]),
Pooling(fshape='all', strides=1, op="avg"),
Dropout(keep=0.8),
Conv(**self.conv_params(1, nout, activation=Softmax()))]
class ModelRunnerNeon():
def __init__(self, args, max_action_no, batch_dimension):
self.args = args
self.train_batch_size = args.train_batch_size
self.discount_factor = args.discount_factor
self.use_gpu_replay_mem = args.use_gpu_replay_mem
self.be = gen_backend(backend='gpu',
batch_size=self.train_batch_size)
self.input_shape = (batch_dimension[1], batch_dimension[2], batch_dimension[3], batch_dimension[0])
self.input = self.be.empty(self.input_shape)
self.input.lshape = self.input_shape # HACK: needed for convolutional networks
self.targets = self.be.empty((max_action_no, self.train_batch_size))
if self.use_gpu_replay_mem:
self.history_buffer = self.be.zeros(batch_dimension, dtype=np.uint8)
self.input_uint8 = self.be.empty(self.input_shape, dtype=np.uint8)
else:
self.history_buffer = np.zeros(batch_dimension, dtype=np.float32)
self.train_net = Model(self.create_layers(max_action_no))
self.cost = GeneralizedCost(costfunc=SumSquared())
# Bug fix
for l in self.train_net.layers.layers:
l.parallelism = 'Disabled'
self.train_net.initialize(self.input_shape[:-1], self.cost)
self.target_net = Model(self.create_layers(max_action_no))
# Bug fix
for l in self.target_net.layers.layers:
l.parallelism = 'Disabled'
self.target_net.initialize(self.input_shape[:-1])
if self.args.optimizer == 'Adam': # Adam
self.optimizer = Adam(beta_1=args.rms_decay,
beta_2=args.rms_decay,
learning_rate=args.learning_rate)
else: # Neon RMSProp
self.optimizer = RMSProp(decay_rate=args.rms_decay,
learning_rate=args.learning_rate)
self.max_action_no = max_action_no
self.running = True
def get_initializer(self, input_size):
dnnInit = self.args.dnn_initializer
if dnnInit == 'xavier':
initializer = Xavier()
elif dnnInit == 'fan_in':
std_dev = 1.0 / math.sqrt(input_size)
initializer = Uniform(low=-std_dev, high=std_dev)
else:
initializer = Gaussian(0, 0.01)
return initializer
def create_layers(self, max_action_no):
layers = []
initializer = self.get_initializer(input_size = 4 * 8 * 8)
layers.append(Conv(fshape=(8, 8, 32), strides=4, init=initializer, bias=initializer, activation=Rectlin()))
initializer = self.get_initializer(input_size = 32 * 4 * 4)
layers.append(Conv(fshape=(4, 4, 64), strides=2, init=initializer, bias=initializer, activation=Rectlin()))
initializer = self.get_initializer(input_size = 64 * 3 * 3)
layers.append(Conv(fshape=(3, 3, 64), strides=1, init=initializer, bias=initializer, activation=Rectlin()))
initializer = self.get_initializer(input_size = 7 * 7 * 64)
layers.append(Affine(nout=512, init=initializer, bias=initializer, activation=Rectlin()))
initializer = self.get_initializer(input_size = 512)
layers.append(Affine(nout=max_action_no, init=initializer, bias=initializer))
return layers
def clip_reward(self, reward):
if reward > self.args.clip_reward_high:
return self.args.clip_reward_high
elif reward < self.args.clip_reward_low:
return self.args.clip_reward_low
else:
return reward
def set_input(self, data):
if self.use_gpu_replay_mem:
self.be.copy_transpose(data, self.input_uint8, axes=(1, 2, 3, 0))
self.input[:] = self.input_uint8 / 255
else:
self.input.set(data.transpose(1, 2, 3, 0).copy())
self.be.divide(self.input, 255, self.input)
def predict(self, history_buffer):
self.set_input(history_buffer)
output = self.train_net.fprop(self.input, inference=True)
return output.T.asnumpyarray()[0]
def print_weights(self):
pass
def train(self, minibatch, replay_memory, learning_rate, debug):
if self.args.prioritized_replay == True:
prestates, actions, rewards, poststates, terminals, replay_indexes, heap_indexes, weights = minibatch
else:
prestates, actions, rewards, poststates, terminals = minibatch
# Get Q*(s, a) with targetNet
self.set_input(poststates)
post_qvalue = self.target_net.fprop(self.input, inference=True).T.asnumpyarray()
if self.args.double_dqn == True:
# Get Q*(s, a) with trainNet
post_qvalue2 = self.train_net.fprop(self.input, inference=True).T.asnumpyarray()
# Get Q(s, a) with trainNet
self.set_input(prestates)
pre_qvalue = self.train_net.fprop(self.input, inference=False)
label = pre_qvalue.asnumpyarray().copy()
for i in range(0, self.train_batch_size):
if self.args.clip_reward:
reward = self.clip_reward(rewards[i])
else:
reward = rewards[i]
if terminals[i]:
label[actions[i], i] = reward
else:
if self.args.double_dqn == True:
max_index = np.argmax(post_qvalue2[i])
label[actions[i], i] = reward + self.discount_factor* post_qvalue[i][max_index]
else:
label[actions[i], i] = reward + self.discount_factor* np.max(post_qvalue[i])
# copy targets to GPU memory
self.targets.set(label)
delta = self.cost.get_errors(pre_qvalue, self.targets)
if self.args.prioritized_replay == True:
delta_value = delta.asnumpyarray()
for i in range(self.train_batch_size):
if debug:
print 'weight[%s]: %.5f, delta: %.5f, newDelta: %.5f' % (i, weights[i], delta_value[actions[i], i], weights[i] * delta_value[actions[i], i])
replay_memory.update_td(heap_indexes[i], abs(delta_value[actions[i], i]))
delta_value[actions[i], i] = weights[i] * delta_value[actions[i], i]
delta.set(delta_value.copy())
if self.args.clip_loss:
self.be.clip(delta, -1.0, 1.0, out = delta)
self.train_net.bprop(delta)
self.optimizer.optimize(self.train_net.layers_to_optimize, epoch=0)
def update_model(self):
# have to serialize also states for batch normalization to work
pdict = self.train_net.get_description(get_weights=True, keep_states=True)
self.target_net.deserialize(pdict, load_states=True)
#print ('Updated target model')
def finish_train(self):
self.running = False
def load(self, file_name):
self.train_net.load_params(file_name)
self.update_model()
def save(self, file_name):
self.train_net.save_params(file_name)
class DeepQNetwork:
def __init__(self, state_size, num_actions, args):
# remember parameters
self.state_size = state_size
self.num_actions = num_actions
self.batch_size = args.batch_size
self.discount_rate = args.discount_rate
self.clip_error = args.clip_error
self.action_count = np.zeros(21)
# create Neon backend
self.be = gen_backend(backend = args.backend,
batch_size = args.batch_size,
rng_seed = args.random_seed,
device_id = args.device_id,
datatype = np.dtype(args.datatype).type,
stochastic_round = args.stochastic_round)
# prepare tensors once and reuse them
self.input_shape = (self.state_size, self.batch_size)
self.input = self.be.empty(self.input_shape)
self.targets = self.be.empty((self.num_actions, self.batch_size))
# create model
layers = self._createLayers(num_actions)
self.model = Model(layers = layers)
self.cost = GeneralizedCost(costfunc = SumSquared())
self.model.initialize(self.input_shape[:-1], self.cost)
if args.optimizer == 'rmsprop':
self.optimizer = RMSProp(learning_rate = args.learning_rate,
decay_rate = args.decay_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adam':
self.optimizer = Adam(learning_rate = args.learning_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adadelta':
self.optimizer = Adadelta(decay = args.decay_rate,
stochastic_round = args.stochastic_round)
else:
assert False, "Unknown optimizer"
# create target model
self.target_steps = args.target_steps
self.train_iterations = 0
if self.target_steps:
self.target_model = Model(layers = self._createLayers(num_actions))
self.target_model.initialize(self.input_shape[:-1])
self.save_weights_prefix = args.save_weights_prefix
else:
self.target_model = self.model
def _createLayers(self, num_actions):
# create network
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = []
# The final hidden layer is fully-connected and consists of 512 rectifier units.
layers.append(Affine(nout=64, init=init_norm, bias=init_norm, activation=Rectlin()))
# The output layer is a fully-connected linear layer with a single output for each valid action.
layers.append(Affine(nout=num_actions, init=init_norm, bias=init_norm))
return layers
def _setInput(self, states):
# change order of axes to match what Neon expects
states = np.transpose(states)
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.input.set(states.copy())
# normalize network input between 0 and 1
# self.be.divide(self.input, 255, self.input)
def train(self, minibatch, epoch):
# expand components of minibatch
prestates, actions, speed_actions, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 2
assert len(poststates.shape) == 2
assert len(actions.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
#print "WE ARE ACTUALLY TRAINING IN HERE"
if self.target_steps and self.train_iterations % self.target_steps == 0:
# HACK: serialize network to disk and read it back to clone
filename = self.save_weights_prefix + "_target.pkl"
save_obj(self.model.serialize(keep_states = False), filename)
self.target_model.load_weights(filename)
# feed-forward pass for poststates to get Q-values
self._setInput(poststates)
postq = self.target_model.fprop(self.input, inference = True)
assert postq.shape == (self.num_actions, self.batch_size)
# calculate max Q-value for each poststate
postq = postq.asnumpyarray()
maxpostq = np.max(postq, axis=0)
#print maxpostq.shape
assert maxpostq.shape == (self.batch_size,)
# feed-forward pass for prestates
self._setInput(prestates)
preq = self.model.fprop(self.input, inference = False)
assert preq.shape == (self.num_actions, self.batch_size)
# make copy of prestate Q-values as targets
targets = preq.asnumpyarray().copy()
# update Q-value targets for actions taken
for i, action in enumerate(actions):
self.action_count[action] += 1
if terminals[i]:
targets[action, i] = float(rewards[i])
if rewards[i] == -1000:
print "######################### action ", action, "should never be sampled again"
print "sampled_terminal"
else:
targets[action, i] = float(rewards[i]) + self.discount_rate * maxpostq[i]
#targets[i,action] = float(rewards[i]) + self.discount_rate * maxpostq[i]
#print "action count", self.action_count
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
#assert np.count_nonzero(deltas.asnumpyarray()) == 32
print "nonzero deltas", np.count_nonzero(deltas.asnumpyarray())
# calculate cost, just in case
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1,1)
print "cost:", cost.asnumpyarray()
# clip errors
#if self.clip_error:
# self.be.clip(deltas, -self.clip_error, self.clip_error, out = deltas)
# perform back-propagation of gradients
self.model.bprop(deltas)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
# increase number of weight updates (needed for target clone interval)
self.train_iterations += 1
def predict(self, states):
# minibatch is full size, because Neon doesn't let change the minibatch size
assert states.shape == (self.batch_size, self.state_size)
# calculate Q-values for the states
self._setInput(states)
qvalues = self.model.fprop(self.input, inference = True)
assert qvalues.shape == (self.num_actions, self.batch_size)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Q-values: " + str(qvalues.asnumpyarray()[:,0]))
# transpose the result, so that batch size is first dimension
return qvalues.T.asnumpyarray()
def load_weights(self, load_path):
self.model.load_weights(load_path)
def save_weights(self, save_path):
save_obj(self.model.serialize(keep_states = True), save_path)
class DeepQNetwork:
def __init__(self, num_actions, args):
# remember parameters
self.num_actions = num_actions
self.batch_size = args.batch_size
self.discount_rate = args.discount_rate
self.history_length = args.history_length
self.screen_dim = (args.screen_height, args.screen_width)
self.clip_error = args.clip_error
self.min_reward = args.min_reward
self.max_reward = args.max_reward
self.batch_norm = args.batch_norm
# create Neon backend
self.be = gen_backend(backend = args.backend,
batch_size = args.batch_size,
rng_seed = args.random_seed,
device_id = args.device_id,
datatype = np.dtype(args.datatype).type,
stochastic_round = args.stochastic_round)
# prepare tensors once and reuse them
self.input_shape = (self.history_length,) + self.screen_dim + (self.batch_size,)
self.input = self.be.empty(self.input_shape)
self.input.lshape = self.input_shape # HACK: needed for convolutional networks
self.targets = self.be.empty((self.num_actions, self.batch_size))
# create model
layers = self._createLayers(num_actions)
self.model = Model(layers = layers)
self.cost = GeneralizedCost(costfunc = SumSquared())
# Bug fix
for l in self.model.layers.layers:
l.parallelism = 'Disabled'
self.model.initialize(self.input_shape[:-1], self.cost)
if args.optimizer == 'rmsprop':
self.optimizer = RMSProp(learning_rate = args.learning_rate,
decay_rate = args.decay_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adam':
self.optimizer = Adam(learning_rate = args.learning_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adadelta':
self.optimizer = Adadelta(decay = args.decay_rate,
stochastic_round = args.stochastic_round)
else:
assert false, "Unknown optimizer"
# create target model
self.train_iterations = 0
if args.target_steps:
self.target_model = Model(layers = self._createLayers(num_actions))
# Bug fix
for l in self.target_model.layers.layers:
l.parallelism = 'Disabled'
self.target_model.initialize(self.input_shape[:-1])
self.save_weights_prefix = args.save_weights_prefix
else:
self.target_model = self.model
self.callback = None
def _createLayers(self, num_actions):
# create network
init_xavier_conv = Xavier(local=True)
init_xavier_affine = Xavier(local=False)
layers = []
# The first hidden layer convolves 32 filters of 8x8 with stride 4 with the input image and applies a rectifier nonlinearity.
layers.append(Conv((8, 8, 32), strides=4, init=init_xavier_conv, activation=Rectlin(), batch_norm=self.batch_norm))
# The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
layers.append(Conv((4, 4, 64), strides=2, init=init_xavier_conv, activation=Rectlin(), batch_norm=self.batch_norm))
# This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
layers.append(Conv((3, 3, 64), strides=1, init=init_xavier_conv, activation=Rectlin(), batch_norm=self.batch_norm))
# The final hidden layer is fully-connected and consists of 512 rectifier units.
layers.append(Affine(nout=512, init=init_xavier_affine, activation=Rectlin(), batch_norm=self.batch_norm))
# The output layer is a fully-connected linear layer with a single output for each valid action.
layers.append(Affine(nout=num_actions, init = init_xavier_affine))
return layers
def _setInput(self, states):
# change order of axes to match what Neon expects
states = np.transpose(states, axes = (1, 2, 3, 0))
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.input.set(states.copy())
# normalize network input between 0 and 1
self.be.divide(self.input, 255, self.input)
def update_target_network(self):
# have to serialize also states for batch normalization to work
pdict = self.model.get_description(get_weights=True, keep_states=True)
self.target_model.deserialize(pdict, load_states=True)
def train(self, minibatch, epoch):
# expand components of minibatch
prestates, actions, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 4
assert len(poststates.shape) == 4
assert len(actions.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
# feed-forward pass for poststates to get Q-values
self._setInput(poststates)
postq = self.target_model.fprop(self.input, inference = True)
assert postq.shape == (self.num_actions, self.batch_size)
# calculate max Q-value for each poststate
maxpostq = self.be.max(postq, axis=0).asnumpyarray()
assert maxpostq.shape == (1, self.batch_size)
# feed-forward pass for prestates
self._setInput(prestates)
preq = self.model.fprop(self.input, inference = False)
assert preq.shape == (self.num_actions, self.batch_size)
# make copy of prestate Q-values as targets
targets = preq.asnumpyarray().copy()
# clip rewards between -1 and 1
rewards = np.clip(rewards, self.min_reward, self.max_reward)
# update Q-value targets for actions taken
for i, action in enumerate(actions):
if terminals[i]:
targets[action, i] = float(rewards[i])
else:
targets[action, i] = float(rewards[i]) + self.discount_rate * maxpostq[0,i]
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
#assert np.count_nonzero(deltas.asnumpyarray()) == 32
# calculate cost, just in case
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1,1)
# clip errors
if self.clip_error:
self.be.clip(deltas, -self.clip_error, self.clip_error, out = deltas)
# perform back-propagation of gradients
self.model.bprop(deltas)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
# increase number of weight updates (needed for stats callback)
self.train_iterations += 1
# calculate statistics
if self.callback:
self.callback.on_train(cost[0,0])
def predict(self, states):
# minibatch is full size, because Neon doesn't let change the minibatch size
assert states.shape == ((self.batch_size, self.history_length,) + self.screen_dim)
# calculate Q-values for the states
self._setInput(states)
qvalues = self.model.fprop(self.input, inference = True)
assert qvalues.shape == (self.num_actions, self.batch_size)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Q-values: " + str(qvalues.asnumpyarray()[:,0]))
# transpose the result, so that batch size is first dimension
return qvalues.T.asnumpyarray()
def load_weights(self, load_path):
self.model.load_params(load_path)
def save_weights(self, save_path):
self.model.save_params(save_path)
class ModelRunnerNeon():
def __init__(self, args, max_action_no, batch_dimension):
self.args = args
self.train_batch_size = args.train_batch_size
self.discount_factor = args.discount_factor
self.use_gpu_replay_mem = args.use_gpu_replay_mem
self.be = gen_backend(backend='gpu', batch_size=self.train_batch_size)
self.input_shape = (batch_dimension[1], batch_dimension[2],
batch_dimension[3], batch_dimension[0])
self.input = self.be.empty(self.input_shape)
self.input.lshape = self.input_shape # HACK: needed for convolutional networks
self.targets = self.be.empty((max_action_no, self.train_batch_size))
if self.use_gpu_replay_mem:
self.history_buffer = self.be.zeros(batch_dimension,
dtype=np.uint8)
self.input_uint8 = self.be.empty(self.input_shape, dtype=np.uint8)
else:
self.history_buffer = np.zeros(batch_dimension, dtype=np.float32)
self.train_net = Model(self.create_layers(max_action_no))
self.cost = GeneralizedCost(costfunc=SumSquared())
# Bug fix
for l in self.train_net.layers.layers:
l.parallelism = 'Disabled'
self.train_net.initialize(self.input_shape[:-1], self.cost)
self.target_net = Model(self.create_layers(max_action_no))
# Bug fix
for l in self.target_net.layers.layers:
l.parallelism = 'Disabled'
self.target_net.initialize(self.input_shape[:-1])
if self.args.optimizer == 'Adam': # Adam
self.optimizer = Adam(beta_1=args.rms_decay,
beta_2=args.rms_decay,
learning_rate=args.learning_rate)
else: # Neon RMSProp
self.optimizer = RMSProp(decay_rate=args.rms_decay,
learning_rate=args.learning_rate)
self.max_action_no = max_action_no
self.running = True
def get_initializer(self, input_size):
dnnInit = self.args.dnn_initializer
if dnnInit == 'xavier':
initializer = Xavier()
elif dnnInit == 'fan_in':
std_dev = 1.0 / math.sqrt(input_size)
initializer = Uniform(low=-std_dev, high=std_dev)
else:
initializer = Gaussian(0, 0.01)
return initializer
def create_layers(self, max_action_no):
layers = []
initializer = self.get_initializer(input_size=4 * 8 * 8)
layers.append(
Conv(fshape=(8, 8, 32),
strides=4,
init=initializer,
bias=initializer,
activation=Rectlin()))
initializer = self.get_initializer(input_size=32 * 4 * 4)
layers.append(
Conv(fshape=(4, 4, 64),
strides=2,
init=initializer,
bias=initializer,
activation=Rectlin()))
initializer = self.get_initializer(input_size=64 * 3 * 3)
layers.append(
Conv(fshape=(3, 3, 64),
strides=1,
init=initializer,
bias=initializer,
activation=Rectlin()))
initializer = self.get_initializer(input_size=7 * 7 * 64)
layers.append(
Affine(nout=512,
init=initializer,
bias=initializer,
activation=Rectlin()))
initializer = self.get_initializer(input_size=512)
layers.append(
Affine(nout=max_action_no, init=initializer, bias=initializer))
return layers
def clip_reward(self, reward):
if reward > self.args.clip_reward_high:
return self.args.clip_reward_high
elif reward < self.args.clip_reward_low:
return self.args.clip_reward_low
else:
return reward
def set_input(self, data):
if self.use_gpu_replay_mem:
self.be.copy_transpose(data, self.input_uint8, axes=(1, 2, 3, 0))
self.input[:] = self.input_uint8 / 255
else:
self.input.set(data.transpose(1, 2, 3, 0).copy())
self.be.divide(self.input, 255, self.input)
def predict(self, history_buffer):
self.set_input(history_buffer)
output = self.train_net.fprop(self.input, inference=True)
return output.T.asnumpyarray()[0]
def print_weights(self):
pass
def train(self, minibatch, replay_memory, learning_rate, debug):
if self.args.prioritized_replay == True:
prestates, actions, rewards, poststates, terminals, replay_indexes, heap_indexes, weights = minibatch
else:
prestates, actions, rewards, poststates, terminals = minibatch
# Get Q*(s, a) with targetNet
self.set_input(poststates)
post_qvalue = self.target_net.fprop(self.input,
inference=True).T.asnumpyarray()
if self.args.double_dqn == True:
# Get Q*(s, a) with trainNet
post_qvalue2 = self.train_net.fprop(
self.input, inference=True).T.asnumpyarray()
# Get Q(s, a) with trainNet
self.set_input(prestates)
pre_qvalue = self.train_net.fprop(self.input, inference=False)
label = pre_qvalue.asnumpyarray().copy()
for i in range(0, self.train_batch_size):
if self.args.clip_reward:
reward = self.clip_reward(rewards[i])
else:
reward = rewards[i]
if terminals[i]:
label[actions[i], i] = reward
else:
if self.args.double_dqn == True:
max_index = np.argmax(post_qvalue2[i])
label[actions[i],
i] = reward + self.discount_factor * post_qvalue[i][
max_index]
else:
label[actions[i],
i] = reward + self.discount_factor * np.max(
post_qvalue[i])
# copy targets to GPU memory
self.targets.set(label)
delta = self.cost.get_errors(pre_qvalue, self.targets)
if self.args.prioritized_replay == True:
delta_value = delta.asnumpyarray()
for i in range(self.train_batch_size):
if debug:
print 'weight[%s]: %.5f, delta: %.5f, newDelta: %.5f' % (
i, weights[i], delta_value[actions[i], i],
weights[i] * delta_value[actions[i], i])
replay_memory.update_td(heap_indexes[i],
abs(delta_value[actions[i], i]))
delta_value[actions[i],
i] = weights[i] * delta_value[actions[i], i]
delta.set(delta_value.copy())
if self.args.clip_loss:
self.be.clip(delta, -1.0, 1.0, out=delta)
self.train_net.bprop(delta)
self.optimizer.optimize(self.train_net.layers_to_optimize, epoch=0)
def update_model(self):
# have to serialize also states for batch normalization to work
pdict = self.train_net.get_description(get_weights=True,
keep_states=True)
self.target_net.deserialize(pdict, load_states=True)
#print ('Updated target model')
def finish_train(self):
self.running = False
def load(self, file_name):
self.train_net.load_params(file_name)
self.update_model()
def save(self, file_name):
self.train_net.save_params(file_name)
Affine(train_set.nfeatures, init, bias=init, activation=Identity())
]
else:
layers = [
LSTM(recurrent_units,
init,
activation=Logistic(),
gate_activation=Tanh(),
reset_cells=False),
RecurrentLast(),
Affine(train_set.nfeatures, init, bias=init, activation=Identity())
]
model_new = Model(layers=layers)
model_new.load_params(args.save_path)
model_new.initialize(dataset=(train_set.nfeatures, seq_len))
num_predict = 200
seed_seq_len = 30
output = np.zeros((train_set.nfeatures, num_predict))
seed = time_series.train[:seed_seq_len]
x = model_new.be.empty((train_set.nfeatures, seq_len))
for s_in in seed:
x.set(s_in.reshape(train_set.nfeatures, seq_len))
y = model_new.fprop(x, inference=False)
for i in range(num_predict):
# Take last prediction and feed into next fprop
pred = y.get()[:, -1]
padding=0,
strides=1,
init=init_uni,
bias=Constant(0),
activation=Tanh()))
layers.append(Pooling(2, strides=2, op='max'))
layers.append(Affine(nout=500, init=init_norm, activation=Rectlin()))
layers.append(Affine(nout=config.ydim, init=init_norm, activation=Softmax()))
# setup cost function as CrossEntropy
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
# initialize model object
network = Model(layers=layers)
network.cost = cost
network.initialize(data, cost)
if config.backend == 'gpu':
start = drv.Event()
end = drv.Event()
num_iterations = config.num_warmup_iters + config.num_timing_iters
forward_time = np.zeros(config.num_timing_iters)
backward_time = np.zeros(config.num_timing_iters)
iter = 0
flag = True
while flag:
for (x, t) in data:
iter += 1
if iter > num_iterations:
flag = False
break
class DQNNeon(Learner):
""" This class is an implementation of the DQN network based on Neon.
The modules that interact with the agent, the replay memory and the
statistic calls are implemented here, taking the individual requirements
of the Lasagne framework into account. The code is adapted from:
https://github.com/tambetm/simple_dqn
Attributes:
input_shape (tuple[int]): Dimension of the network input.
dummy_batch (numpy.ndarray): Dummy batche used to calculate Q-values for single states.
batch_norm (bool): Indicates if normalization is wanted for a certain layer (default=False).
be (neon.backends.nervanagpu.NervanaGPU): Describes the backend for the Neon implementation.
input (neon.backends.nervanagpu.GPUTensor): Definition of network input shape.
targets(neon.backends.nervanagpu.GPUTensor): Definition of network output shape.
model (neon.models.model.Model): Generated Neon model.
target_model (neon.models.model.Model): Generated target Neon model.
cost_func (neon.layers.layer.GeneralizedCost): Cost function for model training.
callback (Statistics): Hook for the statistics object to pass train and test information.
Note:
More attributes of this class are defined in the base class Learner.
"""
def __init__(self, env, args, rng, name = "DQNNeon"):
""" Initializes a network based on the Neon framework.
Args:
env (AtariEnv): The envirnoment in which the agent actuates.
args (argparse.Namespace): All settings either with a default value or set via command line arguments.
rng (mtrand.RandomState): initialized Mersenne Twister pseudo-random number generator.
name (str): The name of the network object.
Note:
This function should always call the base class first to initialize
the common values for the networks.
"""
_logger.info("Initializing new object of type " + str(type(self).__name__))
super(DQNNeon, self).__init__(env, args, rng, name)
self.input_shape = (self.sequence_length,) + self.frame_dims + (self.batch_size,)
self.dummy_batch = np.zeros((self.batch_size, self.sequence_length) + self.frame_dims, dtype=np.uint8)
self.batch_norm = args.batch_norm
self.be = gen_backend(
backend = args.backend,
batch_size = args.batch_size,
rng_seed = args.random_seed,
device_id = args.device_id,
datatype = np.dtype(args.datatype).type,
stochastic_round = args.stochastic_round)
# prepare tensors once and reuse them
self.input = self.be.empty(self.input_shape)
self.input.lshape = self.input_shape # HACK: needed for convolutional networks
self.targets = self.be.empty((self.output_shape, self.batch_size))
# create model
layers = self._create_layer()
self.model = Model(layers = layers)
self.cost_func = GeneralizedCost(costfunc = SumSquared())
# Bug fix
for l in self.model.layers.layers:
l.parallelism = 'Disabled'
self.model.initialize(self.input_shape[:-1], self.cost_func)
self._set_optimizer()
if not self.args.load_weights == None:
self.load_weights(self.args.load_weights)
# create target model
if self.target_update_frequency:
layers = self._create_layer()
self.target_model = Model(layers)
# Bug fix
for l in self.target_model.layers.layers:
l.parallelism = 'Disabled'
self.target_model.initialize(self.input_shape[:-1])
else:
self.target_model = self.model
self.callback = None
_logger.debug("%s" % self)
def _create_layer(self):
""" Build a network consistent with the DeepMind Nature paper. """
_logger.debug("Output shape = %d" % self.output_shape)
# create network
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = []
# The first hidden layer convolves 32 filters of 8x8 with stride 4 with the input image and applies a rectifier nonlinearity.
layers.append(
Conv((8, 8, 32),
strides=4,
init=init_norm,
activation=Rectlin(),
batch_norm=self.batch_norm))
# The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
layers.append(
Conv((4, 4, 64),
strides=2,
init=init_norm,
activation=Rectlin(),
batch_norm=self.batch_norm))
# This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
layers.append(
Conv((3, 3, 64),
strides=1,
init=init_norm,
activation=Rectlin(),
batch_norm=self.batch_norm))
# The final hidden layer is fully-connected and consists of 512 rectifier units.
layers.append(
Affine(
nout=512,
init=init_norm,
activation=Rectlin(),
batch_norm=self.batch_norm))
# The output layer is a fully-connected linear layer with a single output for each valid action.
layers.append(
Affine(
nout= self.output_shape,
init = init_norm))
return layers
def _set_optimizer(self):
""" Initializes the selected optimization algorithm. """
_logger.debug("Optimizer = %s" % str(self.args.optimizer))
if self.args.optimizer == 'rmsprop':
self.optimizer = RMSProp(
learning_rate = self.args.learning_rate,
decay_rate = self.args.decay_rate,
stochastic_round = self.args.stochastic_round)
elif self.args.optimizer == 'adam':
self.optimizer = Adam(
learning_rate = self.args.learning_rate,
stochastic_round = self.args.stochastic_round)
elif self.args.optimizer == 'adadelta':
self.optimizer = Adadelta(
decay = self.args.decay_rate,
stochastic_round = self.args.stochastic_round)
else:
assert false, "Unknown optimizer"
def _prepare_network_input(self, states):
""" Transforms and normalizes the states from one minibatch.
Args:
states (): a set of states with the size of minibatch
"""
_logger.debug("Normalizing and transforming input")
# change order of axes to match what Neon expects
states = np.transpose(states, axes = (1, 2, 3, 0))
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.input.set(states.copy())
# normalize network input between 0 and 1
self.be.divide(self.input, self.grayscales, self.input)
def train(self, minibatch, epoch):
""" Prepare, perform and document a complete train step for one minibatch.
Args:
minibatch (numpy.ndarray): Mini-batch of states, shape=(batch_size,sequence_length,frame_width,frame_height)
epoch (int): Current train epoch
"""
_logger.debug("Complete trainig step for one minibatch")
prestates, actions, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 4
assert len(poststates.shape) == 4
assert len(actions.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
# feed-forward pass for poststates to get Q-values
self._prepare_network_input(poststates)
postq = self.target_model.fprop(self.input, inference = True)
assert postq.shape == (self.output_shape, self.batch_size)
# calculate max Q-value for each poststate
maxpostq = self.be.max(postq, axis=0).asnumpyarray()
assert maxpostq.shape == (1, self.batch_size)
# average maxpostq for stats
maxpostq_avg = maxpostq.mean()
# feed-forward pass for prestates
self._prepare_network_input(prestates)
preq = self.model.fprop(self.input, inference = False)
assert preq.shape == (self.output_shape, self.batch_size)
# make copy of prestate Q-values as targets
targets = preq.asnumpyarray()
# clip rewards between -1 and 1
rewards = np.clip(rewards, self.min_reward, self.max_reward)
# update Q-value targets for each state only at actions taken
for i, action in enumerate(actions):
if terminals[i]:
targets[action, i] = float(rewards[i])
else:
targets[action, i] = float(rewards[i]) + self.discount_rate * maxpostq[0,i]
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
errors = self.cost_func.get_errors(preq, self.targets)
assert errors.shape == (self.output_shape, self.batch_size)
# average error where there is a error (should be 1 in every row)
#TODO: errors_avg = np.sum(errors)/np.size(errors[errors>0.])
# clip errors
if self.clip_error:
self.be.clip(errors, -self.clip_error, self.clip_error, out = errors)
# calculate cost, just in case
cost = self.cost_func.get_cost(preq, self.targets)
assert cost.shape == (1,1)
# perform back-propagation of gradients
self.model.bprop(errors)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
# increase number of weight updates (needed for target clone interval)
self.update_iterations += 1
if self.target_update_frequency and self.update_iterations % self.target_update_frequency == 0:
self._copy_theta()
_logger.info("Network update #%d: Cost = %s, Avg Max Q-value = %s" % (self.update_iterations, str(cost.asnumpyarray()[0][0]), str(maxpostq_avg)))
# update statistics
if self.callback:
self.callback.from_learner(cost.asnumpyarray()[0,0], maxpostq_avg)
def get_Q(self, state):
""" Calculates the Q-values for one mini-batch.
Args:
state(numpy.ndarray): Single state, shape=(sequence_length,frame_width,frame_height).
Returns:
q_values (numpy.ndarray): Results for first element of mini-batch from one forward pass through the network, shape=(self.output_shape,)
"""
_logger.debug("State shape = %s" % str(state.shape))
# minibatch is full size, because Neon doesn't let change the minibatch size
# so we need to run 32 forward steps to get the one we actually want
self.dummy_batch[0] = state
states = self.dummy_batch
assert states.shape == ((self.batch_size, self.sequence_length,) + self.frame_dims)
# calculate Q-values for the states
self._prepare_network_input(states)
qvalues = self.model.fprop(self.input, inference = True)
assert qvalues.shape == (self.output_shape, self.batch_size)
_logger.debug("Qvalues: %s" % (str(qvalues.asnumpyarray()[:,0])))
return qvalues.asnumpyarray()[:,0]
def _copy_theta(self):
""" Copies the weights of the current network to the target network. """
_logger.debug("Copying weights")
pdict = self.model.get_description(get_weights=True, keep_states=True)
self.target_model.deserialize(pdict, load_states=True)
def save_weights(self, target_dir, epoch):
""" Saves the current network parameters to disk.
Args:
target_dir (str): Directory where the network parameters are stored for each episode.
epoch (int): Current epoch.
"""
filename = "%s_%s_%s_%d.prm" % (str(self.args.game.lower()), str(self.args.net_type.lower()), str(self.args.optimizer.lower()), (epoch + 1))
self.model.save_params(os.path.join(target_dir, filename))
def load_weights(self, source_file):
""" Loads the network parameters from a given file.
Args:
source_file (str): Complete path to a file with network parameters.
"""
self.model.load_params(source_file)
init_glorot,
activation=Tanh(),
gate_activation=Logistic(),
reset_cells=True),
RecurrentSum(),
Dropout(keep=0.5),
Affine(nclass, init_glorot, bias=init_glorot, activation=Softmax())
]
# load the weights
print("Initialized the models - ")
model_new = Model(layers=layers)
print("Loading the weights from {0}".format(args.model_weights))
model_new.load_params(args.model_weights)
model_new.initialize(dataset=(sentence_length, batch_size))
# setup buffers before accepting reviews
xdev = be.zeros((sentence_length, 1), dtype=np.int32) # bsz is 1, feature size
xbuf = np.zeros((1, sentence_length), dtype=np.int32)
oov = 2
start = 1
index_from = 3
pad_char = 0
vocab, rev_vocab = pickle.load(open(args.vocab_file, 'rb'))
while True:
line = input('Enter a Review from testData.tsv file \n')
# clean the input
tokens = clean_string(line).strip().split()
class Vectorizer:
model = None
layer = None
patch_width = None
patch_height = None
backend = None
cores = None
generated_backend_object= None
def __init__(self, prm_path=default_prm_path, layer=-4, backend='gpu', cores=32):
print 'Log::Vectorizer:: Initialising Vectorizer'
self.layer = layer
if not os.path.isfile(prm_path):
raise Exception('FileNotFound: Cannot find the file %s' % prm_path)
print 'Log::Vectorizer:: Generating backend, backend: {}'.format(backend)
if backend == 'cpu':
cores = 1
self.cores = cores
self.generated_backend_object = gen_backend(batch_size=self.cores, backend=backend)
self.backend = backend
print 'Log::Vectorizer:: Loading model from %s' % prm_path
model_dict = load_obj(prm_path)
print 'Log::Vectorizer:: Generating model with loaded file'
self.model = Model(model_dict)
# now we are going to extract the middle patch from the image,
# based on the size used to train the model
self.patch_height = model_dict['train_input_shape'][1]
self.patch_width = model_dict['train_input_shape'][2]
print 'Log::Vectorizer:: Initialising Model'
# initialise the model so that internally the arrays are allocated to the correct size
self.model.initialize(model_dict['train_input_shape'])
print 'Log::Vectorizer:: DONE!'
def get_attribute_vector(self, img_path):
if not image_is_local(img_path):
print 'Error::Vectorizer:: File Not Found: The image at %s does not exist' % img_path
return -1
im = imread(img_path).astype(float)
# Fix the image into a flat array organised as [RRRRR..GGGGG..BBBB]
patch_array = self.patch_image(im)
# make an image buffer on host, pad out to batch size
host_buf = np.zeros((3 * self.patch_height * self.patch_width, self.model.be.bsz))
# set the first image to be the image data loaded above
host_buf[:, 0] = patch_array.copy()
# make buffer on the device
dev_buf = self.model.be.zeros((3 * self.patch_height * self.patch_width, self.model.be.bsz))
# copy host buffer to device buffer
dev_buf[:] = host_buf
# Send through the network. Note that in the returned array there
# will be one column for each item in the batch; as we only put data
# in the first item, we only want the first column
predictions = self.model.fprop(dev_buf, True).asnumpyarray()[:, 0]
# print predictions
# Print the activations of the 4th layer from the end of the model
# Note 1: model.layers represents a SingleOutputTree when using GoogLeNet;
# during inference only the main branch (index 0) outputs are considered
# Note 2: in the returned array there will be one column for each item
# in the batch; as we only put data in the first item, we only want the
# first column
return self.model.layers.layers[0].layers[self.layer].outputs.asnumpyarray()[:, 0]
def get_batch_attribute_vectors(self, img_path_array):
"""
Given you are running on a GPU, you batch process self.cores number of images at a time.
:param img_path_array: An array of self.cores number of images
:return: A dict of the paths and their respective attribute vectors
:raise GpuNotSupportedException -- This method will only work when your `self.backend == 'gpu'`
"""
# Ensure that a cpu user is not accessing a GPU command
if self.backend is not 'gpu':
raise GpuNotSupportedException(self.backend)
imgs_to_process, failed_images = self.get_images_to_process(img_path_array)
# make an image buffer on host, pad out to batch size
# Note: self.model.be.bsz == self.cores
host_buf = np.zeros((3 * self.patch_height * self.patch_width, self.model.be.bsz))
for img_index in range(len(imgs_to_process)):
im = imread(imgs_to_process[img_index]).astype(float)
# Fix the image into a flat array organised as [RRRRR..GGGGG..BBBB]
patch_array = self.patch_image(im)
# set the first image to be the image data loaded above
host_buf[:, img_index] = patch_array.copy()
# make buffer on the device
dev_buf = self.model.be.zeros((3 * self.patch_height * self.patch_width, self.model.be.bsz))
# copy host buffer to device buffer
dev_buf[:] = host_buf
# Send through the network.
predictions = self.model.fprop(dev_buf, True).asnumpyarray()[:, 0]
# print predictions
# Print the activations of the 4th layer from the end of the model
# Note 1: model.layers represents a SingleOutputTree when using GoogLeNet;
# during inference only the main branch (index 0) outputs are considered
img_vector_dict = {}
for img_index in range(len(imgs_to_process)):
img_path = imgs_to_process[img_index]
img_vect = self.model.layers.layers[0].layers[self.layer].outputs.asnumpyarray()[:, img_index].tolist()
img_vector_dict[img_path] = img_vect
return img_vector_dict, failed_images
def get_NSEW_batch_attribute_vectors(self, img_path_array):
"""
Given you are running on a GPU, you batch process self.cores number of images at a time.
:param img_path_array: An array of self.cores number of images
:return: A dict of the paths and their respective attribute vectors
:raise GpuNotSupportedException -- This method will only work when your `self.backend == 'gpu'`
"""
# Ensure that a cpu user is not accessing a GPU command
if self.backend is not 'gpu':
raise GpuNotSupportedException(self.backend)
# Ensure there are enough (& not too many images) that are also available locally
imgs_to_process, failed_images = self.get_images_to_process_for_NSEW(img_path_array)
# Shift the focus around the 9 locations with the image
imgs_urls_with_direction = []
patches = []
for local_img_url in imgs_to_process:
im_data = imread(local_img_url).astype(float)
# Get each patch around the image: NW, N, NE, W, mid, E, SW, S, SE
# Fix the image into a flat array organised as [RRRRR..GGGGG..BBBB]
for direction, patch in self.make_NSEW_image_patches(im_data).items():
imgs_urls_with_direction.append(local_img_url+direction)
patches.append(patch)
# make an image buffer on host, pad out to batch size
# Note: self.model.be.bsz == self.cores
# Then fill it with each image patch
host_buf = np.zeros((3 * self.patch_height * self.patch_width, self.model.be.bsz))
for img_index in range(len(patches)):
host_buf[:, img_index] = patches[img_index].copy()
# make buffer on the device
dev_buf = self.model.be.zeros((3 * self.patch_height * self.patch_width, self.model.be.bsz))
# copy host buffer to device buffer
dev_buf[:] = host_buf
# Send through the network.
predictions = self.model.fprop(dev_buf, True).asnumpyarray()[:, 0]
# print predictions
# Print the activations of the 4th layer from the end of the model
# Note 1: model.layers represents a SingleOutputTree when using GoogLeNet;
# during inference only the main branch (index 0) outputs are considered
img_vector_dict = {}
for img_index in range(len(imgs_urls_with_direction)):
img_path = imgs_urls_with_direction[img_index]
img_vect = self.model.layers.layers[0].layers[self.layer].outputs.asnumpyarray()[:, img_index].tolist()
img_vector_dict[img_path] = img_vect
return img_vector_dict, failed_images
# Expects 256x256
def patch_image(self, im):
# convert to BGR
im = im[:, :, ::-1]
# approximately mean-centre it
im = im - [128, 128, 128]
# Finding the co-ordinates for each corner of the centre patch
padY = int(self.patch_height / 2.0)
padX = int(self.patch_width / 2.0)
y = im.shape[0] - 2 * padY
x = im.shape[1] - 2 * padX
col = int(x / 2)
row = int(y / 2)
right = col + self.patch_width
left = col
top = row
bottom = row + self.patch_height
# Cropping the image
patch = im[top:bottom, left:right, :]
# Neon wants the data as a flat array organised as [RRRRR..GGGGG..BBBB]
patch_array = patch.transpose((2, 0, 1)).flatten()
return patch_array
# Expects 256x256
def make_NSEW_image_patches(self, im):
# convert to BGR
im = im[:, :, ::-1]
# approximately mean-centre it
im = im - [128, 128, 128]
# Determine the region of the image not used to process
image_width = im.shape[1]
image_height = im.shape[0]
x_remainder = image_width - 2 * int(self.patch_width / 2.0) # makes it even
y_remainder = image_height - 2 * int(self.patch_height / 2.0)
# Determine the width of the horiz and vertical borders
west_x = 0
mid_x = x_remainder / 2
east_x = image_width - self.patch_width
north_y = 0
mid_y = y_remainder / 2
south_y = image_height - self.patch_height
# Describe the position of the image (based on its top left point of the patch)
# Assuming [0,0] is the top left point in the whole image
factors = {
# North Level
'#NW': [west_x, north_y],
'#N': [mid_x, north_y],
'#NE': [east_x, north_y],
# Mid Level
'#W': [west_x, mid_y],
'#mid': [mid_x, mid_y],
'#E': [east_x, mid_y],
# South Level
'#SW': [west_x, south_y],
'#S': [mid_x, south_y],
'#SE': [east_x, south_y],
}
patch_array_dict = {}
for direction, coord in factors.items():
top = coord[1]
left = coord[0]
bottom = top + self.patch_height
right = left + self.patch_width
# Cropping the image
patch = im[top:bottom, left:right, :]
# Neon wants the data as a flat array organised as [RRRRR..GGGGG..BBBB]
patch_array_dict[direction] = patch.transpose((2, 0, 1)).flatten()
return patch_array_dict
def get_images_to_process(self, img_path_array):
"""
Keeps the first self.cores number of images that can be found locally.
:param img_path_array:
:return: passed_images (array<string>) -- Up to self.cores number of images that it can process
failed_images (dict<string,string>) -- The images that could not be found or excess images
"""
failed_images = {}
local_images = []
for img_path in img_path_array:
if image_is_local(img_path):
local_images.append(img_path)
else:
failed_images[img_path] = 'Image not found locally'
# If there are any more than self.cores number of images, ignore them
for excess_img in local_images[self.cores:]:
failed_images[excess_img] = 'Skipped, more than {} images'.format(self.cores)
return local_images[:self.cores], failed_images
def get_images_to_process_for_NSEW(self, img_path_array):
"""
Keeps the first number of images that can be found locally.
Given that there is enough for 9 of them to be processed.
:param img_path_array:
:return: passed_images (array<string>) -- Up to self.cores number of images that it can process
failed_images (dict<string,string>) -- The images that could not be found or excess images
"""
failed_images = {}
local_images = []
for img_path in img_path_array:
if image_is_local(img_path):
local_images.append(img_path)
else:
failed_images[img_path] = 'Image not found locally'
# If there are any more than self.cores number of images, ignore them
max_local_images = self.cores / 9
for excess_img in local_images[max_local_images:]:
failed_images[excess_img] = 'Skipped, more than {} images, as ({}+1) > {}' \
.format(max_local_images, max_local_images, self.cores)
return local_images[:max_local_images], failed_images
# INPUT: load the model
model_name = raw_input('Specify file name and extension--> ')
if model_name == '':
model_name = 'imdb.p'
# INPIT: how many to validate
how_many_inputs = int(input('How many reviews to validate? (0 for all)--> '))
print ''
# Load model including layers, parameters, and weights
model = Model('[1]model/' + model_name)
# initialize model
model.initialize(dataset=(sentence_length, 1))
# CPU-only buffer
input_numpy = np.zeros((sentence_length, 1), dtype=np.int32)
correct_predictions = 0
neg_files = 0
total_filecount = 0
neg_predictions = 0
pos_predictions = 0
for file in os.listdir('[0]data/test/neg'):
if file.endswith('.txt'):
filename = '[0]data/test/neg/' + str(file)
file = open(filename, 'r')
movie_review = file.read()
class DeepQNetwork:
def __init__(self, num_actions, args):
# remember parameters
self.num_actions = num_actions
self.batch_size = args.batch_size
self.discount_rate = args.discount_rate
self.history_length = args.history_length
self.screen_dim = (args.screen_height, args.screen_width)
self.clip_error = args.clip_error
# create Neon backend
self.be = gen_backend(backend=args.backend,
batch_size=args.batch_size,
rng_seed=args.random_seed,
device_id=args.device_id,
default_dtype=np.dtype(args.datatype).type,
stochastic_round=args.stochastic_round)
# prepare tensors once and reuse them
self.input_shape = (self.history_length, ) + self.screen_dim + (
self.batch_size, )
self.tensor = self.be.empty(self.input_shape)
self.tensor.lshape = self.input_shape # needed for convolutional networks
self.targets = self.be.empty((self.num_actions, self.batch_size))
# create model
layers = self.createLayers(num_actions)
self.model = Model(layers=layers)
self.cost = GeneralizedCost(costfunc=SumSquared())
self.model.initialize(self.tensor.shape, self.cost)
self.optimizer = RMSProp(learning_rate=args.learning_rate,
decay_rate=args.rmsprop_decay_rate,
stochastic_round=args.stochastic_round)
# create target model
self.target_steps = args.target_steps
self.train_iterations = 0
if self.target_steps:
self.target_model = Model(layers=self.createLayers(num_actions))
self.target_model.initialize(self.tensor.shape)
self.save_weights_path = args.save_weights_path
else:
self.target_model = self.model
self.callback = None
def createLayers(self, num_actions):
# create network
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = []
# The first hidden layer convolves 32 filters of 8x8 with stride 4 with the input image and applies a rectifier nonlinearity.
layers.append(
Conv((8, 8, 32), strides=4, init=init_norm, activation=Rectlin()))
# The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
layers.append(
Conv((4, 4, 64), strides=2, init=init_norm, activation=Rectlin()))
# This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
layers.append(
Conv((3, 3, 64), strides=1, init=init_norm, activation=Rectlin()))
# The final hidden layer is fully-connected and consists of 512 rectifier units.
layers.append(Affine(nout=512, init=init_norm, activation=Rectlin()))
# The output layer is a fully-connected linear layer with a single output for each valid action.
layers.append(Affine(nout=num_actions, init=init_norm))
return layers
def setTensor(self, states):
# change order of axes to match what Neon expects
states = np.transpose(states, axes=(1, 2, 3, 0))
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.tensor.set(states.copy())
# normalize network input between 0 and 1
self.be.divide(self.tensor, 255, self.tensor)
def train(self, minibatch, epoch):
# expand components of minibatch
prestates, actions, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 4
assert len(poststates.shape) == 4
assert len(actions.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == actions.shape[0] == rewards.shape[
0] == poststates.shape[0] == terminals.shape[0]
if self.target_steps and self.train_iterations % self.target_steps == 0:
# HACK: push something through network, so that weights exist
self.model.fprop(self.tensor)
# HACK: serialize network to disk and read it back to clone
filename = os.path.join(self.save_weights_path,
"target_network.pkl")
save_obj(self.model.serialize(keep_states=False), filename)
self.target_model.load_weights(filename)
# feed-forward pass for poststates to get Q-values
self.setTensor(poststates)
postq = self.target_model.fprop(self.tensor, inference=True)
assert postq.shape == (self.num_actions, self.batch_size)
# calculate max Q-value for each poststate
maxpostq = self.be.max(postq, axis=0).asnumpyarray()
assert maxpostq.shape == (1, self.batch_size)
# feed-forward pass for prestates
self.setTensor(prestates)
preq = self.model.fprop(self.tensor, inference=False)
assert preq.shape == (self.num_actions, self.batch_size)
# make copy of prestate Q-values as targets
targets = preq.asnumpyarray()
# update Q-value targets for actions taken
for i, action in enumerate(actions):
if terminals[i]:
targets[action, i] = float(rewards[i])
else:
targets[action, i] = float(
rewards[i]) + self.discount_rate * maxpostq[0, i]
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
#assert np.count_nonzero(deltas.asnumpyarray()) == 32
# calculate cost, just in case
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1, 1)
# clip errors
if self.clip_error:
self.be.clip(deltas, -self.clip_error, self.clip_error, out=deltas)
# perform back-propagation of gradients
self.model.bprop(deltas)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
# increase number of weight updates (needed for target clone interval)
self.train_iterations += 1
# calculate statistics
if self.callback:
self.callback.on_train(cost.asnumpyarray()[0, 0])
def predict(self, states):
# minibatch is full size, because Neon doesn't let change the minibatch size
assert states.shape == ((
self.batch_size,
self.history_length,
) + self.screen_dim)
# calculate Q-values for the states
self.setTensor(states)
qvalues = self.model.fprop(self.tensor, inference=True)
assert qvalues.shape == (self.num_actions, self.batch_size)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Q-values: " + str(qvalues.asnumpyarray()[:, 0]))
# find the action with highest q-value
actions = self.be.argmax(qvalues, axis=0)
assert actions.shape == (1, self.batch_size)
# take only the first result
return actions.asnumpyarray()[0, 0]
def getMeanQ(self, states):
assert states.shape == ((
self.batch_size,
self.history_length,
) + self.screen_dim)
# calculate Q-values for the states
self.setTensor(states)
qvalues = self.model.fprop(self.tensor, inference=True)
assert qvalues.shape == (self.num_actions, self.batch_size)
# take maximum Q-value for each state
actions = self.be.max(qvalues, axis=0)
assert actions.astensor().shape == (1, self.batch_size)
# calculate mean Q-value of all states
meanq = self.be.mean(actions, axis=1)
assert meanq.astensor().shape == (1, 1)
# return the mean
return meanq.asnumpyarray()[0, 0]
def load_weights(self, load_path):
self.model.load_weights(load_path)
def save_weights(self, save_path):
save_obj(self.model.serialize(keep_states=True), save_path)
reset_cells=True),
Affine(train_set.vocab_size, init, bias=init2, activation=Softmax())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyMulti(usebits=True))
model = Model(layers=layers)
callbacks = Callbacks(model, **args.callback_args)
opt = RMSProp(decay_rate=0.997,
learning_rate=0.0005,
epsilon=1e-8,
gradient_clip_value=1)
# train model
if not args.test_only:
model.fit(train_set,
optimizer=opt,
num_epochs=num_epochs,
cost=cost,
callbacks=callbacks)
else:
if args.model_file is None:
raise ValueError('To run test only need to specify a model file')
model.initialize(train_set, cost=cost)
test_set = ImageCaptionTest(path=data_path)
sents, targets = test_set.predict(model)
test_set.bleu_score(sents, targets)
prob = prob / (prob.sum() + 1e-6)
return np.argmax(np.random.multinomial(1, prob, 1))
# Set batch size and time_steps to 1 for generation and reset buffers
model.be.bsz = 1
time_steps = 1
num_predict = 1000
layers = [
LSTM(hidden_size, init, activation=Logistic(), gate_activation=Tanh()),
Affine(len(train_set.vocab), init, bias=init, activation=Softmax())
]
model_new = Model(layers=layers)
model_new.load_params(args.save_path)
model_new.initialize(dataset=(train_set.shape[0], time_steps))
# Generate text
text = []
seed_tokens = list('ROMEO:')
x = model_new.be.zeros((len(train_set.vocab), time_steps))
for s in seed_tokens:
x.fill(0)
x[train_set.token_to_index[s], 0] = 1
y = model_new.fprop(x)
for i in range(num_predict):
# Take last prediction and feed into next fprop
pred = sample(y.get()[:, -1])
import os from neon.util.argparser import NeonArgparser from neon.util.persist import load_obj from neon.transforms import Misclassification, CrossEntropyMulti from neon.optimizers import GradientDescentMomentum from neon.layers import GeneralizedCost from neon.models import Model from neon.data import DataLoader, ImageParams # parse the command line arguments (generates the backend) parser = NeonArgparser(__doc__) args = parser.parse_args() # setup data provider test_dir = os.path.join(args.data_dir, 'val') shape = dict(channel_count=3, height=32, width=32) test_params = ImageParams(center=True, flip=False, **shape) common = dict(target_size=1, nclasses=10) test_set = DataLoader(set_name='val', repo_dir=test_dir, media_params=test_params, **common) model = Model(load_obj(args.model_file)) cost = GeneralizedCost(costfunc=CrossEntropyMulti()) opt = GradientDescentMomentum(0.1, 0.9, wdecay=0.0001) model.initialize(test_set, cost=cost) acc = 1.0 - model.eval(test_set, metric=Misclassification())[0] print 'Accuracy: %.1f %% (Top-1)' % (acc*100.0) model.benchmark(test_set, cost=cost, optimizer=opt)
class DeepQNetwork:
def __init__(self, state_size, num_steers, num_speeds, args):
# remember parameters
self.state_size = state_size
self.num_steers = num_steers
self.num_speeds = num_speeds
self.num_actions = num_steers + num_speeds
self.num_layers = args.hidden_layers
self.hidden_nodes = args.hidden_nodes
self.batch_size = args.batch_size
self.discount_rate = args.discount_rate
self.clip_error = args.clip_error
# create Neon backend
self.be = gen_backend(backend = args.backend,
batch_size = args.batch_size,
rng_seed = args.random_seed,
device_id = args.device_id,
datatype = np.dtype(args.datatype).type,
stochastic_round = args.stochastic_round)
# prepare tensors once and reuse them
self.input_shape = (self.state_size, self.batch_size)
self.input = self.be.empty(self.input_shape)
self.targets = self.be.empty((self.num_actions, self.batch_size))
# create model
self.model = Model(layers = self._createLayers())
self.cost = GeneralizedCost(costfunc = SumSquared())
self.model.initialize(self.input_shape[:-1], self.cost)
if args.optimizer == 'rmsprop':
self.optimizer = RMSProp(learning_rate = args.learning_rate,
decay_rate = args.decay_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adam':
self.optimizer = Adam(learning_rate = args.learning_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adadelta':
self.optimizer = Adadelta(decay = args.decay_rate,
stochastic_round = args.stochastic_round)
else:
assert false, "Unknown optimizer"
# create target model
self.target_steps = args.target_steps
self.train_iterations = 0
if self.target_steps:
self.target_model = Model(layers = self._createLayers())
self.target_model.initialize(self.input_shape[:-1])
self.save_weights_prefix = args.save_weights_prefix
else:
self.target_model = self.model
def _createLayers(self):
# create network
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = []
for i in xrange(self.num_layers):
layers.append(Affine(nout=self.hidden_nodes, init=init_norm, activation=Rectlin()))
layers.append(Affine(nout=self.num_actions, init = init_norm))
return layers
def _setInput(self, states):
# change order of axes to match what Neon expects
states = np.transpose(states)
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.input.set(states.copy())
# normalize network input between 0 and 1
#self.be.divide(self.input, 200, self.input)
def train(self, minibatch, epoch = 0):
# expand components of minibatch
prestates, steers, speeds, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 2
assert len(poststates.shape) == 2
assert len(steers.shape) == 1
assert len(speeds.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == steers.shape[0] == speeds.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
if self.target_steps and self.train_iterations % self.target_steps == 0:
# HACK: serialize network to disk and read it back to clone
filename = self.save_weights_prefix + "_target.pkl"
save_obj(self.model.serialize(keep_states = False), filename)
self.target_model.load_weights(filename)
# feed-forward pass for poststates to get Q-values
self._setInput(poststates)
postq = self.target_model.fprop(self.input, inference = True)
assert postq.shape == (self.num_actions, self.batch_size)
# calculate max Q-value for each poststate
postq = postq.asnumpyarray()
maxsteerq = np.max(postq[:self.num_steers,:], axis=0)
assert maxsteerq.shape == (self.batch_size,), "size: %s" % str(maxsteerq.shape)
maxspeedq = np.max(postq[-self.num_speeds:,:], axis=0)
assert maxspeedq.shape == (self.batch_size,)
# feed-forward pass for prestates
self._setInput(prestates)
preq = self.model.fprop(self.input, inference = False)
assert preq.shape == (self.num_actions, self.batch_size)
# make copy of prestate Q-values as targets
# HACK: copy() was needed to make it work on CPU
targets = preq.asnumpyarray().copy()
# update Q-value targets for actions taken
for i, (steer, speed) in enumerate(zip(steers, speeds)):
if terminals[i]:
targets[steer, i] = float(rewards[i])
targets[self.num_steers + speed, i] = float(rewards[i])
else:
targets[steer, i] = float(rewards[i]) + self.discount_rate * maxsteerq[i]
targets[self.num_steers + speed, i] = float(rewards[i]) + self.discount_rate * maxspeedq[i]
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
#assert np.count_nonzero(deltas.asnumpyarray()) == 2 * self.batch_size, str(np.count_nonzero(deltas.asnumpyarray()))
# calculate cost, just in case
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1,1)
#print "cost:", cost.asnumpyarray()
# clip errors
if self.clip_error:
self.be.clip(deltas, -self.clip_error, self.clip_error, out = deltas)
# perform back-propagation of gradients
self.model.bprop(deltas)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
'''
if np.any(rewards < 0):
preqq = preq.asnumpyarray().copy()
self._setInput(prestates)
qvalues = self.model.fprop(self.input, inference = True).asnumpyarray().copy()
indexes = rewards < 0
print "indexes:", indexes
print "preq:", preqq[:, indexes].T
print "preq':", qvalues[:, indexes].T
print "diff:", (qvalues[:, indexes]-preqq[:, indexes]).T
print "steers:", steers[indexes]
print "speeds:", speeds[indexes]
print "rewards:", rewards[indexes]
print "terminals:", terminals[indexes]
print "preq[0]:", preqq[:, 0]
print "preq[0]':", qvalues[:, 0]
print "diff:", qvalues[:, 0] - preqq[:, 0]
print "deltas:", deltas.asnumpyarray()[:, indexes].T
raw_input("Press Enter to continue...")
'''
# increase number of weight updates (needed for target clone interval)
self.train_iterations += 1
def predict(self, states):
# minibatch is full size, because Neon doesn't let change the minibatch size
assert states.shape == (self.batch_size, self.state_size)
# calculate Q-values for the states
self._setInput(states)
qvalues = self.model.fprop(self.input, inference = True)
assert qvalues.shape == (self.num_actions, self.batch_size)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Q-values: " + str(qvalues.asnumpyarray()[:,0]))
# transpose the result, so that batch size is first dimension
return qvalues.T.asnumpyarray()
def load_weights(self, load_path):
self.model.load_weights(load_path)
def save_weights(self, save_path):
save_obj(self.model.serialize(keep_states = True), save_path)
class DeepQNetwork:
def __init__(self, num_actions, args):
# remember parameters
self.num_actions = num_actions
self.batch_size = args.batch_size
self.discount_rate = args.discount_rate
self.history_length = args.history_length
self.screen_dim = (args.screen_height, args.screen_width)
self.clip_error = args.clip_error
self.min_reward = args.min_reward
self.max_reward = args.max_reward
self.batch_norm = args.batch_norm
# create Neon backend
self.be = gen_backend(backend = args.backend,
batch_size = args.batch_size,
rng_seed = args.random_seed,
device_id = args.device_id,
datatype = np.dtype(args.datatype).type,
stochastic_round = args.stochastic_round)
# prepare tensors once and reuse them
self.input_shape = (self.history_length,) + self.screen_dim + (self.batch_size,)
self.input = self.be.empty(self.input_shape)
self.input.lshape = self.input_shape # HACK: needed for convolutional networks
self.targets = self.be.empty((self.num_actions, self.batch_size))
# create model
layers = self._createLayers(num_actions)
self.model = Model(layers = layers)
self.cost = GeneralizedCost(costfunc = SumSquared())
# Bug fix
for l in self.model.layers.layers:
l.parallelism = 'Disabled'
self.model.initialize(self.input_shape[:-1], self.cost)
if args.optimizer == 'rmsprop':
self.optimizer = RMSProp(learning_rate = args.learning_rate,
decay_rate = args.decay_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adam':
self.optimizer = Adam(learning_rate = args.learning_rate,
stochastic_round = args.stochastic_round)
elif args.optimizer == 'adadelta':
self.optimizer = Adadelta(decay = args.decay_rate,
stochastic_round = args.stochastic_round)
else:
assert false, "Unknown optimizer"
# create target model
self.target_steps = args.target_steps
self.train_iterations = 0
if self.target_steps:
self.target_model = Model(layers = self._createLayers(num_actions))
# Bug fix
for l in self.target_model.layers.layers:
l.parallelism = 'Disabled'
self.target_model.initialize(self.input_shape[:-1])
self.save_weights_prefix = args.save_weights_prefix
else:
self.target_model = self.model
self.callback = None
def _createLayers(self, num_actions):
# create network
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = []
# The first hidden layer convolves 32 filters of 8x8 with stride 4 with the input image and applies a rectifier nonlinearity.
layers.append(Conv((8, 8, 32), strides=4, init=init_norm, activation=Rectlin(), batch_norm=self.batch_norm))
# The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
layers.append(Conv((4, 4, 64), strides=2, init=init_norm, activation=Rectlin(), batch_norm=self.batch_norm))
# This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
layers.append(Conv((3, 3, 64), strides=1, init=init_norm, activation=Rectlin(), batch_norm=self.batch_norm))
# The final hidden layer is fully-connected and consists of 512 rectifier units.
layers.append(Affine(nout=512, init=init_norm, activation=Rectlin(), batch_norm=self.batch_norm))
# The output layer is a fully-connected linear layer with a single output for each valid action.
layers.append(Affine(nout=num_actions, init = init_norm))
return layers
def _setInput(self, states):
# change order of axes to match what Neon expects
states = np.transpose(states, axes = (1, 2, 3, 0))
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.input.set(states.copy())
# normalize network input between 0 and 1
self.be.divide(self.input, 255, self.input)
def train(self, minibatch, epoch):
# expand components of minibatch
prestates, actions, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 4
assert len(poststates.shape) == 4
assert len(actions.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
if self.target_steps and self.train_iterations % self.target_steps == 0:
# have to serialize also states for batch normalization to work
pdict = self.model.get_description(get_weights=True, keep_states=True)
self.target_model.deserialize(pdict, load_states=True)
# feed-forward pass for poststates to get Q-values
self._setInput(poststates)
postq = self.target_model.fprop(self.input, inference = True)
assert postq.shape == (self.num_actions, self.batch_size)
# calculate max Q-value for each poststate
maxpostq = self.be.max(postq, axis=0).asnumpyarray()
assert maxpostq.shape == (1, self.batch_size)
# feed-forward pass for prestates
self._setInput(prestates)
preq = self.model.fprop(self.input, inference = False)
assert preq.shape == (self.num_actions, self.batch_size)
# make copy of prestate Q-values as targets
# It seems neccessary for cpu backend.
targets = preq.asnumpyarray().copy()
# clip rewards between -1 and 1
rewards = np.clip(rewards, self.min_reward, self.max_reward)
# update Q-value targets for actions taken
for i, action in enumerate(actions):
if terminals[i]:
targets[action, i] = float(rewards[i])
else:
targets[action, i] = float(rewards[i]) + self.discount_rate * maxpostq[0,i]
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
#assert np.count_nonzero(deltas.asnumpyarray()) == 32
# calculate cost, just in case
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1,1)
# clip errors
if self.clip_error:
self.be.clip(deltas, -self.clip_error, self.clip_error, out = deltas)
# perform back-propagation of gradients
self.model.bprop(deltas)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
# increase number of weight updates (needed for target clone interval)
self.train_iterations += 1
# calculate statistics
if self.callback:
self.callback.on_train(cost[0,0])
def predict(self, states):
# minibatch is full size, because Neon doesn't let change the minibatch size
assert states.shape == ((self.batch_size, self.history_length,) + self.screen_dim)
# calculate Q-values for the states
self._setInput(states)
qvalues = self.model.fprop(self.input, inference = True)
assert qvalues.shape == (self.num_actions, self.batch_size)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Q-values: " + str(qvalues.asnumpyarray()[:,0]))
# transpose the result, so that batch size is first dimension
return qvalues.T.asnumpyarray()
def load_weights(self, load_path):
self.model.load_params(load_path)
def save_weights(self, save_path):
self.model.save_params(save_path)
class DeepQNetwork:
def __init__(self,
num_actions,
batch_size=32,
discount_rate=0.99,
history_length=4,
cols=64,
rows=64,
clip_error=1,
min_reward=-1,
max_reward=1,
batch_norm=False):
self.num_actions = num_actions
self.batch_size = batch_size
self.discount_rate = discount_rate
self.history_length = history_length
self.board_dim = (cols, rows)
self.clip_error = clip_error
self.min_reward = min_reward
self.max_reward = max_reward
self.batch_norm = batch_norm
self.be = gen_backend(backend='gpu',
batch_size=self.batch_size,
datatype=np.dtype('float32').type)
self.input_shape = (self.history_length, ) + self.board_dim + (
self.batch_size, )
self.input = self.be.empty(self.input_shape)
self.input.lshape = self.input_shape # hack from simple_dqn "needed for convolutional networks"
self.targets = self.be.empty((self.num_actions, self.batch_size))
layers = self._createLayers(self.num_actions)
self.model = Model(layers=layers)
self.cost = GeneralizedCost(costfunc=SumSquared())
# for l in self.model.layers.layers:
# l.parallelism = 'Disabled'
self.model.initialize(self.input_shape[:-1], cost=self.cost)
self.optimizer = RMSProp(learning_rate=0.002,
decay_rate=0.95,
stochastic_round=True)
self.train_iterations = 0
self.target_model = Model(layers=self._createLayers(num_actions))
# for l in self.target_model.layers.layers:
# l.parallelism = 'Disabled'
self.target_model.initialize(self.input_shape[:-1])
self.callback = None
def _createLayers(self, num_actions):
init_xavier_conv = Xavier(local=True)
init_xavier_affine = Xavier(local=False)
layers = []
layers.append(
Conv((8, 8, 32),
strides=4,
init=init_xavier_conv,
activation=Rectlin(),
batch_norm=self.batch_norm))
layers.append(
Conv((4, 4, 64),
strides=2,
init=init_xavier_conv,
activation=Rectlin(),
batch_norm=self.batch_norm))
layers.append(
Conv((2, 2, 128),
strides=1,
init=init_xavier_conv,
activation=Rectlin(),
batch_norm=self.batch_norm))
layers.append(
Affine(nout=256,
init=init_xavier_affine,
activation=Rectlin(),
batch_norm=self.batch_norm))
layers.append(Affine(nout=num_actions, init=init_xavier_affine))
return layers
def _setInput(self, states):
states = np.transpose(states, axes=(1, 2, 3, 0))
self.input.set(states.copy())
self.be.add(self.input, 1, self.input)
self.be.divide(self.input, 2, self.input)
def update_target_network(self):
pdict = self.model.get_description(get_weights=True, keep_states=True)
self.target_model.deserialize(pdict, load_states=True)
def train(self, minibatch, epoch):
prestates, actions, rewards, poststates, terminals = minibatch
self._setInput(poststates)
postq = self.target_model.fprop(self.input, inference=True)
assert postq.shape == (self.num_actions, self.batch_size)
maxpostq = self.be.max(postq, axis=0).asnumpyarray()
assert maxpostq.shape == (1, self.batch_size)
self._setInput(prestates)
preq = self.model.fprop(self.input, inference=False)
assert preq.shape == (self.num_actions, self.batch_size)
targets = preq.asnumpyarray().copy()
rewards = np.clip(rewards, -1, 1)
for i, action in enumerate(actions):
if terminals[i]:
targets[action, i] = float(rewards[i])
else:
targets[action, i] = float(
rewards[i]) + self.discount_rate * maxpostq[0, i]
self.targets.set(targets)
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1, 1)
if self.clip_error:
self.be.clip(deltas, -self.clip_error, self.clip_error, out=deltas)
self.model.bprop(deltas)
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
self.train_iterations += 1
self.callback.on_train(cost[0, 0])
def predict(self, states):
assert states.shape == ((
self.batch_size,
self.history_length,
) + self.board_dim)
self._setInput(states)
qvalues = self.model.fprop(self.input, inference=True)
assert qvalues.shape == (self.num_actions, self.batch_size)
return qvalues.T.asnumpyarray()
def load_weights(self, load_path):
self.model.load_params(load_path)
def save_weights(self, save_path):
self.model.save_params(save_path)
def main():
# larger batch sizes may not fit on GPU
parser = NeonArgparser(__doc__, default_overrides={'batch_size': 4})
parser.add_argument("--bench", action="store_true", help="run benchmark instead of training")
parser.add_argument("--num_classes", type=int, default=12, help="number of classes in the annotation")
parser.add_argument("--height", type=int, default=256, help="image height")
parser.add_argument("--width", type=int, default=512, help="image width")
args = parser.parse_args(gen_be=False)
# check that image dimensions are powers of 2
if((args.height & (args.height - 1)) != 0):
raise TypeError("Height must be a power of 2.")
if((args.width & (args.width - 1)) != 0):
raise TypeError("Width must be a power of 2.")
(c, h, w) = (args.num_classes, args.height, args.width)
# need to use the backend with the new upsampling layer implementation
be = NervanaGPU_Upsample(rng_seed=args.rng_seed,
device_id=args.device_id)
# set batch size
be.bsz = args.batch_size
# couple backend to global neon object
NervanaObject.be = be
shape = dict(channel_count=3, height=h, width=w, subtract_mean=False)
train_params = ImageParams(center=True, flip=False,
scale_min=min(h, w), scale_max=min(h, w),
aspect_ratio=0, **shape)
test_params = ImageParams(center=True, flip=False,
scale_min=min(h, w), scale_max=min(h, w),
aspect_ratio=0, **shape)
common = dict(target_size=h*w, target_conversion='read_contents',
onehot=False, target_dtype=np.uint8, nclasses=args.num_classes)
train_set = PixelWiseImageLoader(set_name='train', repo_dir=args.data_dir,
media_params=train_params,
shuffle=False, subset_percent=100,
index_file=os.path.join(args.data_dir, 'train_images.csv'),
**common)
val_set = PixelWiseImageLoader(set_name='val', repo_dir=args.data_dir,media_params=test_params,
index_file=os.path.join(args.data_dir, 'val_images.csv'), **common)
# initialize model object
layers = gen_model(c, h, w)
segnet_model = Model(layers=layers)
# configure callbacks
callbacks = Callbacks(segnet_model, eval_set=val_set, **args.callback_args)
opt_gdm = GradientDescentMomentum(1.0e-6, 0.9, wdecay=0.0005, schedule=Schedule())
opt_biases = GradientDescentMomentum(2.0e-6, 0.9, schedule=Schedule())
opt_bn = GradientDescentMomentum(1.0e-6, 0.9, schedule=Schedule())
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases, 'BatchNorm': opt_bn})
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
if args.bench:
segnet_model.initialize(train_set, cost=cost)
segnet_model.benchmark(train_set, cost=cost, optimizer=opt)
sys.exit(0)
else:
segnet_model.fit(train_set, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
# get the trained segnet model outputs for valisation set
outs_val = segnet_model.get_outputs(val_set)
with open('outputs.pkl', 'w') as fid:
pickle.dump(outs_val, fid, -1)
class DeepQNetwork:
def __init__(self, num_actions, args):
# remember parameters
self.num_actions = num_actions
self.batch_size = args.batch_size
self.discount_rate = args.discount_rate
self.history_length = args.history_length
self.screen_dim = (args.screen_height, args.screen_width)
self.clip_error = args.clip_error
# create Neon backend
self.be = gen_backend(backend = args.backend,
batch_size = args.batch_size,
rng_seed = args.random_seed,
device_id = args.device_id,
default_dtype = np.dtype(args.datatype).type,
stochastic_round = args.stochastic_round)
# prepare tensors once and reuse them
self.input_shape = (self.history_length,) + self.screen_dim + (self.batch_size,)
self.tensor = self.be.empty(self.input_shape)
self.tensor.lshape = self.input_shape # needed for convolutional networks
self.targets = self.be.empty((self.num_actions, self.batch_size))
# create model
layers = self.createLayers(num_actions)
self.model = Model(layers = layers)
self.cost = GeneralizedCost(costfunc = SumSquared())
self.model.initialize(self.tensor.shape, self.cost)
self.optimizer = RMSProp(learning_rate = args.learning_rate,
decay_rate = args.rmsprop_decay_rate,
stochastic_round = args.stochastic_round)
# create target model
self.target_steps = args.target_steps
self.train_iterations = 0
if self.target_steps:
self.target_model = Model(layers = self.createLayers(num_actions))
self.target_model.initialize(self.tensor.shape)
self.save_weights_path = args.save_weights_path
else:
self.target_model = self.model
self.callback = None
def createLayers(self, num_actions):
# create network
init_norm = Gaussian(loc=0.0, scale=0.01)
layers = []
# The first hidden layer convolves 32 filters of 8x8 with stride 4 with the input image and applies a rectifier nonlinearity.
layers.append(Conv((8, 8, 32), strides=4, init=init_norm, activation=Rectlin()))
# The second hidden layer convolves 64 filters of 4x4 with stride 2, again followed by a rectifier nonlinearity.
layers.append(Conv((4, 4, 64), strides=2, init=init_norm, activation=Rectlin()))
# This is followed by a third convolutional layer that convolves 64 filters of 3x3 with stride 1 followed by a rectifier.
layers.append(Conv((3, 3, 64), strides=1, init=init_norm, activation=Rectlin()))
# The final hidden layer is fully-connected and consists of 512 rectifier units.
layers.append(Affine(nout=512, init=init_norm, activation=Rectlin()))
# The output layer is a fully-connected linear layer with a single output for each valid action.
layers.append(Affine(nout = num_actions, init = init_norm))
return layers
def setTensor(self, states):
# change order of axes to match what Neon expects
states = np.transpose(states, axes = (1, 2, 3, 0))
# copy() shouldn't be necessary here, but Neon doesn't work otherwise
self.tensor.set(states.copy())
# normalize network input between 0 and 1
self.be.divide(self.tensor, 255, self.tensor)
def train(self, minibatch, epoch):
# expand components of minibatch
prestates, actions, rewards, poststates, terminals = minibatch
assert len(prestates.shape) == 4
assert len(poststates.shape) == 4
assert len(actions.shape) == 1
assert len(rewards.shape) == 1
assert len(terminals.shape) == 1
assert prestates.shape == poststates.shape
assert prestates.shape[0] == actions.shape[0] == rewards.shape[0] == poststates.shape[0] == terminals.shape[0]
if self.target_steps and self.train_iterations % self.target_steps == 0:
# HACK: push something through network, so that weights exist
self.model.fprop(self.tensor)
# HACK: serialize network to disk and read it back to clone
filename = os.path.join(self.save_weights_path, "target_network.pkl")
save_obj(self.model.serialize(keep_states = False), filename)
self.target_model.load_weights(filename)
# feed-forward pass for poststates to get Q-values
self.setTensor(poststates)
postq = self.target_model.fprop(self.tensor, inference = True)
assert postq.shape == (self.num_actions, self.batch_size)
# calculate max Q-value for each poststate
maxpostq = self.be.max(postq, axis=0).asnumpyarray()
assert maxpostq.shape == (1, self.batch_size)
# feed-forward pass for prestates
self.setTensor(prestates)
preq = self.model.fprop(self.tensor, inference = False)
assert preq.shape == (self.num_actions, self.batch_size)
# make copy of prestate Q-values as targets
targets = preq.asnumpyarray()
# update Q-value targets for actions taken
for i, action in enumerate(actions):
if terminals[i]:
targets[action, i] = float(rewards[i])
else:
targets[action, i] = float(rewards[i]) + self.discount_rate * maxpostq[0,i]
# copy targets to GPU memory
self.targets.set(targets)
# calculate errors
deltas = self.cost.get_errors(preq, self.targets)
assert deltas.shape == (self.num_actions, self.batch_size)
#assert np.count_nonzero(deltas.asnumpyarray()) == 32
# calculate cost, just in case
cost = self.cost.get_cost(preq, self.targets)
assert cost.shape == (1,1)
# clip errors
if self.clip_error:
self.be.clip(deltas, -self.clip_error, self.clip_error, out = deltas)
# perform back-propagation of gradients
self.model.bprop(deltas)
# perform optimization
self.optimizer.optimize(self.model.layers_to_optimize, epoch)
# increase number of weight updates (needed for target clone interval)
self.train_iterations += 1
# calculate statistics
if self.callback:
self.callback.on_train(cost.asnumpyarray()[0,0])
def predict(self, states):
# minibatch is full size, because Neon doesn't let change the minibatch size
assert states.shape == ((self.batch_size, self.history_length,) + self.screen_dim)
# calculate Q-values for the states
self.setTensor(states)
qvalues = self.model.fprop(self.tensor, inference = True)
assert qvalues.shape == (self.num_actions, self.batch_size)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Q-values: " + str(qvalues.asnumpyarray()[:,0]))
# find the action with highest q-value
actions = self.be.argmax(qvalues, axis = 0)
assert actions.shape == (1, self.batch_size)
# take only the first result
return actions.asnumpyarray()[0,0]
def getMeanQ(self, states):
assert states.shape == ((self.batch_size, self.history_length,) + self.screen_dim)
# calculate Q-values for the states
self.setTensor(states)
qvalues = self.model.fprop(self.tensor, inference = True)
assert qvalues.shape == (self.num_actions, self.batch_size)
# take maximum Q-value for each state
actions = self.be.max(qvalues, axis = 0)
assert actions.astensor().shape == (1, self.batch_size)
# calculate mean Q-value of all states
meanq = self.be.mean(actions, axis = 1)
assert meanq.astensor().shape == (1, 1)
# return the mean
return meanq.asnumpyarray()[0,0]
def load_weights(self, load_path):
self.model.load_weights(load_path)
def save_weights(self, save_path):
save_obj(self.model.serialize(keep_states = True), save_path)
valmetric = TopKMisclassification(k=5)
# dummy optimizer for benchmarking
# training implementation coming soon
opt_gdm = GradientDescentMomentum(0.0, 0.0)
opt_biases = GradientDescentMomentum(0.0, 0.0)
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
# setup cost function as CrossEntropy
cost = Multicost(costs=[GeneralizedCost(costfunc=CrossEntropyMulti()),
GeneralizedCost(costfunc=CrossEntropyMulti()),
GeneralizedCost(costfunc=CrossEntropyMulti())],
weights=[1, 0., 0.]) # We only want to consider the CE of the main path
assert os.path.exists(args.model_file), 'script requires the trained weights file'
model.load_params(args.model_file)
model.initialize(test, cost)
print 'running speed benchmark...'
model.benchmark(test, cost, opt)
print '\nCalculating performance on validation set...'
test.reset()
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
(1.0-mets[1])*100,
(1.0-mets[2])*100)
Conv((3, 3, 256),
init=Gaussian(scale=0.03),
activation=Rectlin(),
padding=1),
Conv((3, 3, 256),
init=Gaussian(scale=0.03),
activation=Rectlin(),
padding=1),
Pooling(3, strides=2),
Affine(nout=4096, init=Gaussian(scale=0.01), activation=Rectlin()),
Affine(nout=4096, init=Gaussian(scale=0.01), activation=Rectlin()),
Affine(nout=1000, init=Gaussian(scale=0.01), activation=Softmax())
]
weight_sched = Schedule([22, 44, 65], (1 / 250.)**(1 / 3.))
opt_gdm = GradientDescentMomentum(0.01,
0.0,
wdecay=0.0005,
schedule=weight_sched)
opt = MultiOptimizer({'default': opt_gdm})
model = Model(layers=layers, optimizer=opt)
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
model.initialize(train, cost=cost)
b = Benchmark(model)
print("Forward and backward")
res = b.time(train, niterations=5)
b.print_stats(res, nskip=2)
encoder2 = Affine(nout=config.encoder_size[1], init=init_norm,
activation=Logistic(), name='encoder2')
decoder2 = Affine(nout=config.encoder_size[0], init=init_norm,
activation=Logistic(), name='decoder2')
encoder3 = Affine(nout=config.encoder_size[2], init=init_norm,
activation=Logistic(), name='encoder3')
decoder3 = Affine(nout=config.encoder_size[1], init=init_norm,
activation=Logistic(), name='decoder3')
classifier = Affine(nout=config.ydim, init=init_norm, activation=Softmax())
cost_reconst = GeneralizedCost(costfunc=SumSquared())
cost_classification = GeneralizedCost(costfunc=CrossEntropyMulti())
# Setting model layers for AE1
AE1 = Model([encoder1, decoder1])
AE1.cost = cost_reconst
AE1.initialize(data, cost_reconst)
# AE1.optimizer = optimizer_default
measure_time(data, AE1, config, 'AE1')
# Setting model layers for AE2
# It has an extra encoder layer compared to what AE should really be. This is
# done to avoid saving the outputs for each AE.
AE2_mimic = Model([encoder1, encoder2, decoder2])
AE2_mimic.cost = cost_reconst
AE2_mimic.initialize(data, cost_reconst)
# Learning rates for extra layers that should not be updated are set to zero.
# opt = MultiOptimizer({'default': optimizer_default,
# 'encoder1': optimizer_helper})
# AE2_mimic.optimizer = opt
measure_time(data, AE2_mimic, config, 'AE2', create_target=True)
def test_model_serialize(backend_default, data):
(X_train, y_train), (X_test, y_test), nclass = load_mnist(path=data)
train_set = ArrayIterator([X_train, X_train],
y_train,
nclass=nclass,
lshape=(1, 28, 28))
init_norm = Gaussian(loc=0.0, scale=0.01)
# initialize model
path1 = Sequential([
Conv((5, 5, 16),
init=init_norm,
bias=Constant(0),
activation=Rectlin()),
Pooling(2),
Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())
])
path2 = Sequential([
Affine(nout=100,
init=init_norm,
bias=Constant(0),
activation=Rectlin()),
Dropout(keep=0.5),
Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())
])
layers = [
MergeMultistream(layers=[path1, path2], merge="stack"),
Affine(nout=20, init=init_norm, batch_norm=True, activation=Rectlin()),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))
]
tmp_save = 'test_model_serialize_tmp_save.pickle'
mlp = Model(layers=layers)
mlp.optimizer = GradientDescentMomentum(learning_rate=0.1,
momentum_coef=0.9)
mlp.cost = GeneralizedCost(costfunc=CrossEntropyBinary())
mlp.initialize(train_set, cost=mlp.cost)
n_test = 3
num_epochs = 3
# Train model for num_epochs and n_test batches
for epoch in range(num_epochs):
for i, (x, t) in enumerate(train_set):
x = mlp.fprop(x)
delta = mlp.cost.get_errors(x, t)
mlp.bprop(delta)
mlp.optimizer.optimize(mlp.layers_to_optimize, epoch=epoch)
if i > n_test:
break
# Get expected outputs of n_test batches and states of all layers
outputs_exp = []
pdicts_exp = [l.get_params_serialize() for l in mlp.layers_to_optimize]
for i, (x, t) in enumerate(train_set):
outputs_exp.append(mlp.fprop(x, inference=True))
if i > n_test:
break
# Serialize model
mlp.save_params(tmp_save, keep_states=True)
# Load model
mlp = Model(layers=layers)
mlp.load_params(tmp_save)
outputs = []
pdicts = [l.get_params_serialize() for l in mlp.layers_to_optimize]
for i, (x, t) in enumerate(train_set):
outputs.append(mlp.fprop(x, inference=True))
if i > n_test:
break
# Check outputs, states, and params are the same
for output, output_exp in zip(outputs, outputs_exp):
assert np.allclose(output.get(), output_exp.get())
for pd, pd_exp in zip(pdicts, pdicts_exp):
for s, s_e in zip(pd['states'], pd_exp['states']):
if isinstance(s, list): # this is the batch norm case
for _s, _s_e in zip(s, s_e):
assert np.allclose(_s, _s_e)
else:
assert np.allclose(s, s_e)
for p, p_e in zip(pd['params'], pd_exp['params']):
assert type(p) == type(p_e)
if isinstance(p, list): # this is the batch norm case
for _p, _p_e in zip(p, p_e):
assert np.allclose(_p, _p_e)
elif isinstance(p, np.ndarray):
assert np.allclose(p, p_e)
else:
assert p == p_e
os.remove(tmp_save)
# input image parameters - channel_count is 3 for color images, 1 for B/W
shape = dict(channel_count=3, height=h, width=w, subtract_mean=False)
test_params = ImageParams(center=True, flip=False,
scale_min=min(h, w), scale_max=min(h, w),
aspect_ratio=0, **shape)
common = dict(target_size=h*w, target_conversion='read_contents',
onehot=False, target_dtype=np.uint8, nclasses=c)
data_dir = args.image_path
test_set = PixelWiseImageLoader(set_name='test', repo_dir=data_dir,media_params=test_params,
index_file=os.path.join(data_dir, 'test_images.csv'), **common)
# initialize model object
segnet_model = Model(layers=gen_model(c, h, w))
segnet_model.initialize(test_set)
# load up the serialized model
model_desc = ModelDescription(load_obj(args.save_model_file))
for layer in segnet_model.layers_to_optimize:
name = layer.name
trained_layer = model_desc.getlayer(name)
layer.load_weights(trained_layer)
fig = plt.figure()
if args.display:
plt.ion()
im1 = None
im2 = None
bias=Constant(0), activation=Tanh()))
layers.append(Pooling(2, strides=2, op='max'))
# cannot be 50!!! should be multiple of 4
layers.append(Conv((5, 5, 52), padding=0, strides=1, init=init_uni,
bias=Constant(0), activation=Tanh()))
layers.append(Pooling(2, strides=2, op='max'))
layers.append(Affine(nout=500, init=init_norm, activation=Rectlin()))
layers.append(Affine(nout=config.ydim, init=init_norm, activation=Softmax()))
# setup cost function as CrossEntropy
cost = GeneralizedCost(costfunc=CrossEntropyMulti())
# initialize model object
network = Model(layers=layers)
network.cost = cost
network.initialize(data, cost)
if config.backend == 'gpu':
start = drv.Event()
end = drv.Event()
num_iterations = config.num_warmup_iters + config.num_timing_iters
forward_time = np.zeros(config.num_timing_iters)
backward_time = np.zeros(config.num_timing_iters)
iter = 0
flag = True
while flag:
for (x, t) in data:
iter += 1
if iter > num_iterations:
flag = False
break
if not args.resume:
# build the model from scratch and run it
# Now construct the model
branch_nodes = [BranchNode(name='branch{}'.format(i)) for i in (0, 1)]
main1 = main_branch(branch_nodes)
aux1 = aux_branch(branch_nodes[0], ind=1)
aux2 = aux_branch(branch_nodes[1], ind=2)
model = Model(layers=Tree([main1, aux1, aux2], alphas=[1.0, 0.3, 0.3]))
else:
# load up the save model
model = Model('serialize_test_2.pkl')
model.initialize(train, cost=cost)
# configure callbacks
callbacks = Callbacks(model, progress_bar=True, output_file='temp1.h5',
serialize=1, history=3, save_path='serialize_test.pkl')
lr_sched = PolySchedule(total_epochs=10, power=0.5)
opt_gdm = GradientDescentMomentum(0.01, 0.9, wdecay=0.0002, schedule=lr_sched)
opt_biases = GradientDescentMomentum(0.02, 0.9, schedule=lr_sched)
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
if not args.resume:
# fit the model for 3 epochs
model.fit(train, optimizer=opt, num_epochs=3, cost=cost, callbacks=callbacks)
train.reset()
"""
prob = prob / (prob.sum() + 1e-6)
return np.argmax(np.random.multinomial(1, prob, 1))
# Set batch size and time_steps to 1 for generation and reset buffers
model.be.bsz = 1
time_steps = 1
num_predict = 1000
layers = [
LSTM(hidden_size, init, activation=Logistic(), gate_activation=Tanh()),
Affine(len(train_set.vocab), init, bias=init, activation=Softmax())
]
model_new = Model(layers=layers)
model_new.load_params(args.save_path)
model_new.initialize(dataset=(train_set.shape[0], time_steps))
# Generate text
text = []
seed_tokens = list('ROMEO:')
x = model_new.be.zeros((len(train_set.vocab), time_steps))
for s in seed_tokens:
x.fill(0)
x[train_set.token_to_index[s], 0] = 1
y = model_new.fprop(x)
for i in range(num_predict):
# Take last prediction and feed into next fprop
pred = sample(y.get()[:, -1])
def test_conv_rnn(backend_default):
train_shape = (1, 17, 142)
be = backend_default
inp = be.array(be.rng.randn(np.prod(train_shape), be.bsz))
delta = be.array(be.rng.randn(10, be.bsz))
init_norm = Gaussian(loc=0.0, scale=0.01)
bilstm = DeepBiLSTM(128,
init_norm,
activation=Rectlin(),
gate_activation=Rectlin(),
depth=1,
reset_cells=True)
birnn_1 = DeepBiRNN(128,
init_norm,
activation=Rectlin(),
depth=1,
reset_cells=True,
batch_norm=False)
birnn_2 = DeepBiRNN(128,
init_norm,
activation=Rectlin(),
depth=2,
reset_cells=True,
batch_norm=False)
bibnrnn = DeepBiRNN(128,
init_norm,
activation=Rectlin(),
depth=1,
reset_cells=True,
batch_norm=True)
birnnsum = DeepBiRNN(128,
init_norm,
activation=Rectlin(),
depth=1,
reset_cells=True,
batch_norm=False,
bi_sum=True)
rnn = Recurrent(128,
init=init_norm,
activation=Rectlin(),
reset_cells=True)
lstm = LSTM(128,
init_norm,
activation=Rectlin(),
gate_activation=Rectlin(),
reset_cells=True)
gru = GRU(128,
init_norm,
activation=Rectlin(),
gate_activation=Rectlin(),
reset_cells=True)
rlayers = [bilstm, birnn_1, birnn_2, bibnrnn, birnnsum, rnn, lstm, gru]
for rl in rlayers:
layers = [
Conv((2, 2, 4),
init=init_norm,
activation=Rectlin(),
strides=dict(str_h=2, str_w=4)),
Pooling(2, strides=2),
Conv((3, 3, 4),
init=init_norm,
batch_norm=True,
activation=Rectlin(),
strides=dict(str_h=1, str_w=2)),
rl,
RecurrentMean(),
Affine(nout=10, init=init_norm, activation=Rectlin()),
]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.initialize(train_shape, cost)
model.fprop(inp)
model.bprop(delta)
if return_sequences is True:
layers = [
LSTM(hidden, init, activation=Logistic(), gate_activation=Tanh(), reset_cells=False),
Affine(train_set.nfeatures, init, bias=init, activation=Identity())
]
else:
layers = [
LSTM(hidden, init, activation=Logistic(), gate_activation=Tanh(), reset_cells=False),
RecurrentLast(),
Affine(train_set.nfeatures, init, bias=init, activation=Identity())
]
model_new = Model(layers=layers)
model_new.load_params(args.save_path)
model_new.initialize(dataset=(train_set.nfeatures, seq_len))
output = np.zeros((train_set.nfeatures, num_predict))
seed = time_series.train[:seed_seq_len]
x = model_new.be.empty((train_set.nfeatures, seq_len))
for s_in in seed:
x.set(s_in.reshape(train_set.nfeatures, seq_len))
y = model_new.fprop(x, inference=False)
for i in range(num_predict):
# Take last prediction and feed into next fprop
pred = y.get()[:, -1]
output[:, i] = pred
x[:] = pred.reshape(train_set.nfeatures, seq_len)
y = model_new.fprop(x, inference=False)
class InceptionModel(object):
"""
Container for the inception-like test model
"""
def __init__(self):
self.in_shape = (3, 299, 299)
self.nout = 100
self.pool3s1p1 = dict(fshape=3, padding=1, strides=1, op='avg')
self.pool3s2p0 = dict(fshape=3, strides=2, op='max')
layers = self.main_branch(nout=self.nout)
self.cost = GeneralizedCost(costfunc=CrossEntropyMulti())
self.model = Model(layers=layers)
self.model.initialize(self.in_shape, cost=self.cost)
self.layers = layers
@staticmethod
def conv_params(fsize,
nfm,
padding='SAME',
strides=1,
activation=Rectlin(),
batch_norm=True):
fsize = fsize if isinstance(fsize, tuple) else (fsize, fsize)
fshape = fsize + (nfm, )
padding = {
'pad_h': (fsize[0] // 2 if padding == 'SAME' else 0),
'pad_w': (fsize[1] // 2 if padding == 'SAME' else 0),
'pad_d': 0
}
strides = {'str_h': strides, 'str_w': strides, 'str_d': 1}
return dict(fshape=fshape,
strides=strides,
activation=activation,
padding=padding,
batch_norm=batch_norm,
init=Kaiming(local=True))
def inception(self, kvals, b2fsz=5):
(p1, p2, p3, p4) = kvals
branch1 = [Conv(**self.conv_params(1, p1[0], padding=0, strides=1))]
branch2 = [
Conv(**self.conv_params(1, p2[0], padding=0, strides=1)),
Conv(**self.conv_params(b2fsz, p2[1], padding='SAME', strides=1))
],
branch3 = [
Conv(**self.conv_params(1, p3[0], padding=0, strides=1)),
Conv(**self.conv_params(3, p3[1], padding='SAME', strides=1)),
Conv(**self.conv_params(3, p3[1], padding='SAME', strides=1))
]
branch4 = [
Pooling(**self.pool3s1p1),
Conv(**self.conv_params(1, p4[0], padding=0, strides=1))
]
return MergeBroadcast(layers=[branch1, branch2, branch3, branch4],
merge="depth")
def inception_inception(self, kvals):
(p1, p2, p3, p4) = kvals
branch1 = [Conv(**self.conv_params(1, p1[0], padding=0, strides=1))]
i2_branch1 = [
Conv(**self.conv_params((1, 3), p2[1], padding='SAME', strides=1))
]
i2_branch2 = [
Conv(**self.conv_params((3, 1), p2[1], padding='SAME', strides=1))
]
branch2 = [
Conv(**self.conv_params(1, p2[0], padding=0, strides=1)),
MergeBroadcast(layers=[i2_branch1, i2_branch2], merge="depth")
]
i3_branch1 = [
Conv(**self.conv_params((1, 3), p3[2], padding='SAME', strides=1))
]
i3_branch2 = [
Conv(**self.conv_params((3, 1), p3[2], padding='SAME', strides=1))
]
branch3 = [
Conv(**self.conv_params(1, p3[0], padding=0, strides=1)),
Conv(**self.conv_params(3, p3[1], padding='SAME', strides=1)),
MergeBroadcast(layers=[i3_branch1, i3_branch2], merge="depth")
]
branch4 = [
Pooling(**self.pool3s1p1),
Conv(**self.conv_params(1, p4[0], padding=0, strides=1))
]
return MergeBroadcast(layers=[branch1, branch2, branch3, branch4],
merge="depth")
def main_branch(self, nout=100):
return [
Conv(**self.conv_params(3, 32, strides=2, padding=0)),
Pooling(**self.pool3s2p0),
Conv(**self.conv_params(1, 80, strides=1, padding=0)),
Pooling(**self.pool3s2p0),
self.inception([(64, ), (48, 64), (64, 96), (32, )], b2fsz=5),
self.inception([(64, ), (48, 64), (64, 96), (64, )], b2fsz=5),
self.inception_inception([(64, ), (64, 64), (64, 64, 64),
(192, )]),
self.inception_inception([(64, ), (64, 64), (64, 64, 64),
(192, )]),
Pooling(fshape='all', strides=1, op="avg"),
Dropout(keep=0.8),
Conv(**self.conv_params(1, nout, activation=Softmax()))
]
init_glorot,
activation=Tanh(),
gate_activation=Logistic(),
reset_cells=True),
RecurrentSum(),
Dropout(keep=0.5),
Affine(nclass, init_glorot, bias=init_glorot, activation=Softmax())
]
# load the weights
print("Initialized the models - ")
model_new = Model(layers=layers)
print("Loading the weights from {0}".format(args.model_weights))
model_new.load_params(args.model_weights)
model_new.initialize(dataset=(sentence_length, batch_size))
# setup buffers before accepting reviews
xdev = be.zeros((sentence_length, 1), dtype=np.int32) # bsz is 1, feature size
xbuf = np.zeros((1, sentence_length), dtype=np.int32)
oov = 2
start = 1
index_from = 3
pad_char = 0
vocab, rev_vocab = pickle.load(open(args.vocab_file, 'rb'))
while True:
line = input('Enter a Review from testData.tsv file \n')
# clean the input
tokens = clean_string(line).strip().split()
def test_fw_bw_no_cost_or_optimizer():
model = Model(test_layers)
model.initialize(test_dataset)
b = Benchmark(model=model)
with pytest.raises(RuntimeError):
b.time(test_dataset, niterations=1)
# model initialization
image_path = Sequential([Affine(hidden_size, init, bias=Constant(val=0.0))])
sent_path = Sequential([Affine(hidden_size, init, name='sent')])
layers = [
MergeMultistream(layers=[image_path, sent_path], merge="recurrent"),
Dropout(keep=0.5),
LSTM(hidden_size, init, activation=Logistic(), gate_activation=Tanh(), reset_cells=True),
Affine(train_set.vocab_size, init, bias=init2, activation=Softmax())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyMulti(usebits=True))
model = Model(layers=layers)
callbacks = Callbacks(model, **args.callback_args)
opt = RMSProp(decay_rate=0.997, learning_rate=0.0005, epsilon=1e-8, gradient_clip_value=1)
# train model
if not args.test_only:
model.fit(train_set, optimizer=opt, num_epochs=num_epochs, cost=cost, callbacks=callbacks)
else:
if args.model_file is None:
raise ValueError('To run test only need to specify a model file')
model.initialize(train_set, cost=cost)
test_set = ImageCaptionTest(path=data_path)
sents, targets = test_set.predict(model)
test_set.bleu_score(sents, targets)
# dummy optimizer for benchmarking
# training implementation coming soon
opt_gdm = GradientDescentMomentum(0.0, 0.0)
opt_biases = GradientDescentMomentum(0.0, 0.0)
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases})
# setup cost function as CrossEntropy
cost = Multicost(
costs=[
GeneralizedCost(costfunc=CrossEntropyMulti()),
GeneralizedCost(costfunc=CrossEntropyMulti()),
GeneralizedCost(costfunc=CrossEntropyMulti())
],
weights=[1, 0., 0.]) # We only want to consider the CE of the main path
assert os.path.exists(
args.model_file), 'script requires the trained weights file'
model.load_params(args.model_file)
model.initialize(test, cost)
print 'running speed benchmark...'
model.benchmark(test, cost, opt)
print '\nCalculating performance on validation set...'
test.reset()
mets = model.eval(test, metric=valmetric)
print 'Validation set metrics:'
print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (
mets[0], (1.0 - mets[1]) * 100, (1.0 - mets[2]) * 100)
def test_reshape_layer_model(backend_default, fargs):
"""
test cases:
- conv before RNNs
- conv after RNNs
- conv after LUT
"""
np.random.seed(seed=0)
nin, nout, bsz = fargs
be = backend_default
be.bsz = bsz
input_size = (nin, be.bsz)
init = Uniform(-0.1, 0.1)
g_uni = GlorotUniform()
inp_np = np.random.rand(nin, be.bsz)
delta_np = np.random.rand(nout, be.bsz)
inp = be.array(inp_np)
delta = be.array(delta_np)
conv_lut_1 = [
LookupTable(vocab_size=2000, embedding_dim=400, init=init),
Reshape(reshape=(4, 100, -1)),
Conv((3, 3, 16), init=init),
LSTM(64, g_uni, activation=Tanh(),
gate_activation=Logistic(), reset_cells=True),
RecurrentSum(),
Affine(nout, init, bias=init, activation=Softmax())
]
conv_lut_2 = [
LookupTable(vocab_size=1000, embedding_dim=400, init=init),
Reshape(reshape=(4, 50, -1)),
Conv((3, 3, 16), init=init),
Pooling(2, strides=2),
Affine(nout=nout, init=init, bias=init, activation=Softmax()),
]
conv_rnn_1 = [
LookupTable(vocab_size=2000, embedding_dim=400, init=init),
LSTM(64, g_uni, activation=Tanh(),
gate_activation=Logistic(), reset_cells=True),
Reshape(reshape=(4, 32, -1)),
Conv((3, 3, 16), init=init),
Affine(nout, init, bias=init, activation=Softmax())
]
conv_rnn_2 = [
LookupTable(vocab_size=2000, embedding_dim=400, init=init),
Recurrent(64, g_uni, activation=Tanh(), reset_cells=True),
Reshape(reshape=(4, -1, 32)),
Conv((3, 3, 16), init=init),
Affine(nout, init, bias=init, activation=Softmax())
]
lut_sum_1 = [
LookupTable(vocab_size=1000, embedding_dim=128, init=init),
RecurrentSum(),
Affine(nout=nout, init=init, bias=init, activation=Softmax()),
]
lut_birnn_1 = [
LookupTable(vocab_size=1000, embedding_dim=200, init=init),
DeepBiRNN(32, init=GlorotUniform(), batch_norm=True, activation=Tanh(),
reset_cells=True, depth=1),
Reshape((4, 32, -1)),
Conv((3, 3, 16), init=init),
Affine(nout=nout, init=init, bias=init, activation=Softmax())
]
layers_test = [conv_lut_1, conv_lut_2, conv_rnn_1, conv_rnn_2, lut_sum_1, lut_birnn_1]
for lg in layers_test:
model = Model(layers=lg)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
model.initialize(input_size, cost)
model.fprop(inp)
model.bprop(delta)
args = parser.parse_args() # load the cnn model gen_backend(batch_size=1, backend='cpu') # gen_backend(batch_size=32, backend='gpu') model_dict = load_obj(args.prm_name) model = Model(model_dict) # now we are going to extract the middle patch from the image, # based on the size used to train the model patch_height = model_dict['train_input_shape'][1] patch_width = model_dict['train_input_shape'][2] # initialise the model so that internally the arrays are allocated to the correct size model.initialize(model_dict['train_input_shape']) # load the image im = imread(args.image).astype(float) # convert to BGR im = im[:, :, ::-1] # approximately mean-centre it im = im - [128,128,128] # Finding the co-ordinates for each corner of the centre patch padY = int(patch_height / 2.0) padX = int(patch_width / 2.0) y = im.shape[0] - 2 * padY
