def setUp(self):
# Layer size
self.n_in = 28 * 28 # Input size
self.n_h1 = 1000 # Hidden Layer 1 size
self.n_h2 = 1000 # Hidden Layer 2 size
self.n_out = 10 # Output size
# self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.device = torch.device("cpu")
self.model = model.FullyConnected(self.n_in, self.n_h1, self.n_h2, self.n_out, device=self.device)
shuffle=False)
# NOTE: Don't change these settings
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# NOTE: Don't change these settings
# Layer size
N_in = 28 * 28 # Input size
N_h1 = 256 # Hidden Layer 1 size
N_h2 = 256 # Hidden Layer 2 size
N_out = 10 # Output size
# Learning rate
lr = 0.001
# init model
net = model.FullyConnected(N_in, N_h1, N_h2, N_out, device=device)
# TODO: Define number of epochs
N_epoch = 7 # Or keep it as is
# TODO: Training and Validation Loop
# >>> for n epochs
## >>> for all mini batches
### >>> net.train(...)
## at the end of each training epoch
## >>> net.eval(...)
for epoch in range(N_epoch):
for batch_idx, (inputs, labels) in enumerate(train_loader):
inputs = inputs.view(-1, N_in)
net.train(inputs, labels, lr)
""" Bathc len code does not work with enumerate
count=1
for i in train_data_loader:
print(i[1].size())
batch_len=list(i[1].size())
print(batch_len[0])
image_printer_elemental(i[0],i[1],batch_len[0])
if count == 5:
break
count=count+1
"""
net = model.FullyConnected(28 * 28, 256, 256, 10)
EPOCHS = 1
""" TO view images and test the loops
for i in range(EPOCHS):
for batch_idx,(data,label) in enumerate(train_data_loader):
for img_tensor,label_tensor in zip(data,label):
image_printer_elemental(img_tensor,label_tensor)
break
"""
#small tester block
"""
for i in range(EPOCHS):
for batch_idx,(inputs,label) in enumerate(train_data_loader):
#print("Inputs shape",inputs.size())
inputs_size_arr=list(inputs.size())
