when i m training resnet modelin pytorch i got the traing loss is constant in every time …

pytorch:

elif self.model_type == ‘resnet’:

```
self.model = models.resnet50()
self.model.adaptiveavgpool = nn.AdaptiveAvgPool2d(self.model.fc.out_features)
self.model.fc = nn.Linear(in_features=self.model.fc.in_features, out_features=self.output_classes)
self.model.activation = nn.Softmax(self.model.fc)
```

keras:

elif self.model_type in [‘resnet50’]:

# self.model.layers.pop()

model_output = self.model.output

model_output = GlobalAveragePooling2D()(model_output)

predictions = Dense(self.output_classes, activation=‘softmax’)(model_output)

self.model = Model(self.model.input, predictions)

we are doing changing keras to pytorch…so we have an issue on that…

can you please give your suggestions in this…tpoic???

i got this like

Epoch: [0] Train Loss: [1.636952] Valid Loss: [1.576530]

Epoch: [1] Train Loss: [1.636952] Valid Loss: [1.404914]

Epoch: [2] Train Loss: [1.636952] Valid Loss: [1.395322]

Epoch: [3] Train Loss: [1.636952] Valid Loss: [1.391647]

Epoch: [4] Train Loss: [1.636952] Valid Loss: [1.390293]

Epoch: [5] Train Loss: [1.636952] Valid Loss: [1.393284]

Epoch: [6] Train Loss: [1.636952] Valid Loss: [1.395637]

Epoch: [7] Train Loss: [1.636952] Valid Loss: [1.396015]

train the model:

for epoch in range(num_epochs):

```
# set the model in train mode
self.model.train()
self.model.optimizer.zero_grad()
# training_input_data = training_input_data.resize_([training_batch_size, 3, 224, 224])
training_ground_truth = training_ground_truth.resize_([training_batch_size])
self.model = self.model.double()
outputs = self.model(training_input_data.double())
sm=torch.nn.Softmax()
probabilities=sm(outputs)
print("predicted probabilities while testing",probabilities)
loss = self.model.criterion(outputs, training_ground_truth.long())
loss.backward()
self.model.optimizer.step()
train_losses.append(loss.item()
```