def test_rnn_no_memory_pass():
T.manual_seed(1111)
input_size = 100
hidden_size = 100
rnn_type = 'gru'
num_layers = 3
num_hidden_layers = 5
dropout = 0.2
nr_cells = 5000
cell_size = 17
sparse_reads = 3
gpu_id = -1
debug = True
lr = 0.001
sequence_max_length = 10
batch_size = 10
cuda = gpu_id
clip = 20
length = 13
rnn = SAM(input_size=input_size,
hidden_size=hidden_size,
rnn_type=rnn_type,
num_layers=num_layers,
num_hidden_layers=num_hidden_layers,
dropout=dropout,
nr_cells=nr_cells,
cell_size=cell_size,
sparse_reads=sparse_reads,
gpu_id=gpu_id,
debug=debug)
optimizer = optim.Adam(rnn.parameters(), lr=lr)
optimizer.zero_grad()
input_data, target_output = generate_data(batch_size, length, input_size,
cuda)
target_output = target_output.transpose(0, 1).contiguous()
(chx, mhx, rv) = (None, None, None)
outputs = []
for x in range(6):
output, (chx, mhx, rv), v = rnn(input_data, (chx, mhx, rv),
pass_through_memory=False)
output = output.transpose(0, 1)
outputs.append(output)
output = functools.reduce(lambda x, y: x + y, outputs)
loss = criterion((output), target_output)
loss.backward()
T.nn.utils.clip_grad_norm(rnn.parameters(), clip)
optimizer.step()
assert target_output.size() == T.Size([27, 10, 100])
assert chx[0].size() == T.Size([num_hidden_layers, 10, 100])
# assert mhx['memory'].size() == T.Size([10,12,17])
assert rv == None
def test_rnn_n():
T.manual_seed(1111)
input_size = 100
hidden_size = 100
rnn_type = 'gru'
num_layers = 3
num_hidden_layers = 5
dropout = 0.2
nr_cells = 200
cell_size = 17
read_heads = 2
sparse_reads = 4
temporal_reads = 3
gpu_id = -1
debug = True
lr = 0.001
sequence_max_length = 10
batch_size = 10
cuda = gpu_id
clip = 20
length = 13
rnn = SDNC(input_size=input_size,
hidden_size=hidden_size,
rnn_type=rnn_type,
num_layers=num_layers,
num_hidden_layers=num_hidden_layers,
dropout=dropout,
nr_cells=nr_cells,
cell_size=cell_size,
read_heads=read_heads,
sparse_reads=sparse_reads,
temporal_reads=temporal_reads,
gpu_id=gpu_id,
debug=debug)
optimizer = optim.Adam(rnn.parameters(), lr=lr)
optimizer.zero_grad()
input_data, target_output = generate_data(batch_size, length, input_size,
cuda)
target_output = target_output.transpose(0, 1).contiguous()
output, (chx, mhx, rv), v = rnn(input_data, None)
output = output.transpose(0, 1)
loss = criterion((output), target_output)
loss.backward()
T.nn.utils.clip_grad_norm_(rnn.parameters(), clip)
optimizer.step()
assert target_output.size() == T.Size([27, 10, 100])
assert chx[0].size() == T.Size([num_hidden_layers, 10, 100])
# assert mhx['memory'].size() == T.Size([10,12,17])
assert rv.size() == T.Size([10, 34])
def test_rnn_1():
T.manual_seed(1111)
input_size = 100
hidden_size = 100
rnn_type = 'lstm'
num_layers = 1
num_hidden_layers = 1
dropout = 0
nr_cells = 100
cell_size = 10
read_heads = 1
sparse_reads = 2
gpu_id = -1
debug = True
lr = 0.001
sequence_max_length = 10
batch_size = 10
cuda = gpu_id
clip = 10
length = 10
rnn = SAM(input_size=input_size,
hidden_size=hidden_size,
rnn_type=rnn_type,
num_layers=num_layers,
num_hidden_layers=num_hidden_layers,
dropout=dropout,
nr_cells=nr_cells,
cell_size=cell_size,
read_heads=read_heads,
sparse_reads=sparse_reads,
gpu_id=gpu_id,
debug=debug)
optimizer = optim.Adam(rnn.parameters(), lr=lr)
rnn = DNI(rnn, optim=optimizer)
optimizer.zero_grad()
input_data, target_output = generate_data(batch_size, length, input_size,
cuda)
target_output = target_output.transpose(0, 1).contiguous()
output, (chx, mhx, rv), v = rnn(input_data, None)
output = output.transpose(0, 1)
loss = criterion((output), target_output)
loss.backward()
T.nn.utils.clip_grad_norm(rnn.parameters(), clip)
assert target_output.size() == T.Size([21, 10, 100])
assert chx[0][0][0].size() == T.Size([10, 100])
# assert mhx['memory'].size() == T.Size([10,1,1])
assert rv.size() == T.Size([10, 10])
