I am trying to train a 1-D ConvNet for time series classification as shown in this paper (refer to FCN om Fig. 1b) https://arxiv.org/pdf/1611.06455.pdf
The Keras implementation is giving me vastly superior performance. Could someone explain why is that the case?
The code for Pytorch is as follow:
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv1d(x_train.shape[1], 128, 8)
self.bnorm1 = nn.BatchNorm1d(128)
self.conv2 = nn.Conv1d(128, 256, 5)
self.bnorm2 = nn.BatchNorm1d(256)
self.conv3 = nn.Conv1d(256, 128, 3)
self.bnorm3 = nn.BatchNorm1d(128)
self.dense = nn.Linear(128, nb_classes)
def forward(self, x):
c1=self.conv1(x)
b1 = F.relu(self.bnorm1(c1))
c2=self.conv2(b1)
b2 = F.relu(self.bnorm2(c2))
c3=self.conv3(b2)
b3 = F.relu(self.bnorm3(c3))
output = torch.mean(b3, 2)
dense1=self.dense(output)
return F.softmax(dense1)
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.5, momentum=0.99)
losses=[]
for t in range(1000):
y_pred_1= model(x_train.float())
loss_1 = criterion(y_pred_1, y_train.long())
print(t, loss_1.item())
optimizer.zero_grad()
loss_1.backward()
optimizer.step()
For comparison, I use the following code for Keras:
x = keras.layers.Input(x_train.shape[1:])
conv1 = keras.layers.Conv1D(128, 8, padding='valid')(x)
conv1 = keras.layers.BatchNormalization()(conv1)
conv1 = keras.layers.Activation('relu')(conv1)
conv2 = keras.layers.Conv1D(256, 5, padding='valid')(conv1)
conv2 = keras.layers.BatchNormalization()(conv2)
conv2 = keras.layers.Activation('relu')(conv2)
conv3 = keras.layers.Conv1D(128, 3, padding='valid')(conv2)
conv3 = keras.layers.BatchNormalization()(conv3)
conv3 = keras.layers.Activation('relu')(conv3)
full = keras.layers.GlobalAveragePooling1D()(conv3)
out = keras.layers.Dense(nb_classes, activation='softmax')(full)
model = keras.models.Model(inputs=x, outputs=out)
optimizer = keras.optimizers.SGD(lr=0.5, decay=0.0, momentum=0.99)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
hist = model.fit(x_train, Y_train, batch_size=x_train.shape[0], nb_epoch=2000)
The only difference I see between the two is the initialization but however, the results are just vastly different. For reference, I use the same preprocessing as follows for both the datasets, with a subtle difference in input shapes, for Pytorch (Batch_Size, Channels, Length) and for Keras: (Batch_Size, Length, Channels).
