def torch_summarize(model, show_weights=True, show_parameters=True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + ' (\n'
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ')'
return tmpstr
batch_size = params['batch_size']
n_classes = 10
input_channels = 3 * 32
input_channels = 1
seq_length = data['Frame_len'] * 3 * 32
total_step = len(training_set)
learning_rate = opt.force_learning_rate
#model = simpleLSTM(input_size, hidden_size, num_layers, output_size)
model = TCN(input_channels,
n_classes, [25] * 8,
kernel_size=7,
dropout=0.00)
print(torch_summarize(model))
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
sum_done = False
for epoch in range(max_epochs):
# Training
model.train()
for i, (skeleton, label) in enumerate(training_generator):
skeleton = skeleton.reshape(-1, input_channels,
seq_length).to(device)
label = label.to(device)