@kcturgutlu can you share your notebook of which you’ve taken the screenshot? I tried following along but get an error in lr_find():
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-50-d81c6bd29d71> in <module>()
----> 1 learn.lr_find()
~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/learner.py in lr_find(self, start_lr, end_lr, wds)
135 layer_opt = self.get_layer_opt(start_lr, wds)
136 self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), end_lr)
--> 137 self.fit_gen(self.model, self.data, layer_opt, 1)
138 self.load('tmp')
139
~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, metrics, callbacks, **kwargs)
87 n_epoch = sum_geom(cycle_len if cycle_len else 1, cycle_mult, n_cycle)
88 fit(model, data, n_epoch, layer_opt.opt, self.crit,
---> 89 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, **kwargs)
90
91 def get_layer_groups(self): return self.models.get_layer_groups()
~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/model.py in fit(model, data, epochs, opt, crit, metrics, callbacks, **kwargs)
82 for (*x,y) in t:
83 batch_num += 1
---> 84 loss = stepper.step(V(x),V(y))
85 avg_loss = avg_loss * avg_mom + loss * (1-avg_mom)
86 debias_loss = avg_loss / (1 - avg_mom**batch_num)
~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/model.py in step(self, xs, y)
38 def step(self, xs, y):
39 xtra = []
---> 40 output = self.m(*xs)
41 if isinstance(output,(tuple,list)): output,*xtra = output
42 self.opt.zero_grad()
~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
323 for hook in self._forward_pre_hooks.values():
324 hook(self, input)
--> 325 result = self.forward(*input, **kwargs)
326 for hook in self._forward_hooks.values():
327 hook_result = hook(self, input, result)
<ipython-input-40-d4690f427dbe> in forward(self, x_cat, x_cont)
134
135 def forward(self, x_cat, x_cont):
--> 136 x = [emb(x_cat[:, i]) for i, emb in enumerate(self.embs)] # takes necessary emb vectors
137 x = torch.cat(x, 1) ## concatenate along axis = 1 (columns - side by side) # this is our input from cats
138 x = self.emb_drop(x) ## apply dropout to elements of embedding tensor
<ipython-input-40-d4690f427dbe> in <listcomp>(.0)
134
135 def forward(self, x_cat, x_cont):
--> 136 x = [emb(x_cat[:, i]) for i, emb in enumerate(self.embs)] # takes necessary emb vectors
137 x = torch.cat(x, 1) ## concatenate along axis = 1 (columns - side by side) # this is our input from cats
138 x = self.emb_drop(x) ## apply dropout to elements of embedding tensor
~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
323 for hook in self._forward_pre_hooks.values():
324 hook(self, input)
--> 325 result = self.forward(*input, **kwargs)
326 for hook in self._forward_hooks.values():
327 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/sparse.py in forward(self, input)
101 input, self.weight,
102 padding_idx, self.max_norm, self.norm_type,
--> 103 self.scale_grad_by_freq, self.sparse
104 )
105
~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/_functions/thnn/sparse.py in forward(cls, ctx, indices, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
55
56 if indices.dim() == 1:
---> 57 output = torch.index_select(weight, 0, indices)
58 else:
59 output = torch.index_select(weight, 0, indices.view(-1))
TypeError: torch.index_select received an invalid combination of arguments - got (torch.FloatTensor, int, torch.cuda.LongTensor), but expected (torch.FloatTensor source, int dim, torch.LongTensor index)