Hey there, I am trying to reproduce a Keras code in PyTorch can anyone help me out.

```
base_inception = InceptionV3(weights='imagenet', include_top=False,
input_shape=(299, 299, 3))
# Add a global spatial average pooling layer
out = base_inception.output
out = GlobalAveragePooling2D()(out)
out = Dense(512, activation='relu')(out)
out = Dense(512, activation='relu')(out)
total_classes = y_train_ohe.shape[1]
predictions = Dense(total_classes, activation='softmax')(out)
model = Model(inputs=base_inception.input, outputs=predictions)
# only if we want to freeze layers
for layer in base_inception.layers:
layer.trainable = False
# Compile
model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
```

I tried it out but I couldn’t work out the Global Average / Average Pooling part

```
model_ft = models.inception_v3(pretrained=True)
for param in model_ft.parameters():
param.requires_grad = False
#num_ftrs = model_ft.classifier[6].in_features
#model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
model_ft.aux_logits = False
num_ftrs = model_ft.fc.in_features
class inception_v3_see_smart(nn.Module):
def __init__(self, originalModel):
super(inception_v3_see_smart, self).__init__()
self.Model = originalModel
self.adaptive_pool = nn.AvgPool2d(2)
#self.conv1 = nn.Conv2d(1000, 2000, 3)
self.dense1 = nn.Linear(512,512)
self.dense2 = nn.Linear(512,62)
def forward(self, x):
x = self.Model(x)
print(x.shape)
x = self.adaptive_pool(x)
x = F.relu(self.dense1(x))
x = F.relu(self.dense2(x))
return x
model_ft = inception_v3_see_smart(model_ft)
print(model_ft)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
```

Any help is really appreciated!

@ptrblck