old_contents: from tqdm import tqdm, trange
  import torch
  from torch.nn import MSELoss
  from torch.autograd import Variable
  from torch.optim import SGD
  
  from . import objectives
  
  
  class Model(object):
  
      def __init__(self, *layers):
          self.layers = list(layers)
          self.build()
  
      @property
      def params(self):
          return [p for layer in self.layers for p in layer.params]
  
      def build(self):
          for prev_layer, next_layer in zip(self.layers[:-1], self.layers[1:]):
              next_layer.build(prev_layer.output_dim)
  
      def add(self, layer):
          self.layers.append(layer)
  
      def forward(self, x):
          y = self.layers[0].forward(x)
          for layer in self.layers[1:]:
              y = layer.forward(y)
          return y
  
      def fit(self, X, y, loss, optimizer, batch_size=32, n_epochs=10):
          loss = objectives.get(loss)
          optimizer.params = self.params
          n_samples, *_ = X.size()
          history = {'loss': []}
          begin = min(batch_size, n_samples)
          end = n_samples + (n_samples % batch_size) + 1
  
          for epoch in range(n_epochs):  #tqdm(range(n_epochs), desc='Training', position=0):
              epoch_bar = tqdm(range(begin, end, batch_size),
                               desc=f'Epoch {epoch+1:2}')
              loss_sum = 0
              for ibatch, split in enumerate(epoch_bar, start=1):
                  X_batch = Variable(X[(split - batch_size):split])
                  y_batch = Variable(y[(split - batch_size):split])
  
                  out_batch = self.forward(X_batch)
                  loss_value = loss(y_batch, out_batch)
                  loss_value.backward()
                  optimizer.step()
                  loss_sum += loss_value.data[0]
                  epoch_bar.set_postfix(loss=f'{loss_sum/ibatch:.4f}')
              history['loss'].append(loss_value.data[0])
  
          return history
  
new_contents: from tqdm import tqdm, trange
  import torch
  from torch.nn import MSELoss
  from torch.autograd import Variable
  from torch.optim import SGD
  
  from . import objectives
  
  
  class Model(object):
  
      def __init__(self, *layers):
          self.layers = list(layers)
          self.build()
  
      @property
      def params(self):
          return [p for layer in self.layers for p in layer.params]
  
      def build(self):
          for prev_layer, next_layer in zip(self.layers[:-1], self.layers[1:]):
              next_layer.build(prev_layer.output_dim)
  
      def add(self, layer):
          self.layers.append(layer)
  
      def forward(self, x):
          y = self.layers[0].forward(x)
          for layer in self.layers[1:]:
              y = layer.forward(y)
          return y
  
      def fit(self, X, y, loss, optimizer, batch_size=32, n_epochs=10):
          loss = objectives.get(loss)
          optimizer.params = self.params
          n_samples, *_ = X.size()
          history = {'loss': []}
          begin = min(batch_size, n_samples)
          end = n_samples + (n_samples % batch_size) + 1
  
          for epoch in range(n_epochs):
              epoch_bar = tqdm(range(begin, end, batch_size),
                               desc=f'Epoch {epoch+1:2}')
              loss_sum = 0
              for ibatch, split in enumerate(epoch_bar, start=1):
                  X_batch = Variable(X[(split - batch_size):split])
                  y_batch = Variable(y[(split - batch_size):split])
  
                  out_batch = self.forward(X_batch)
                  loss_value = loss(y_batch, out_batch)
                  loss_value.backward()
                  optimizer.step()
                  loss_sum += loss_value.data[0]
                  epoch_bar.set_postfix(loss=f'{loss_sum/ibatch:.4f}')
              history['loss'].append(loss_value.data[0])
  
          return history
  
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