fix(dbn): Fixes adding missing DBN layers when they are not supplied.

docs/conf.py

@@ -27,10 +27,10 @@
 author = "Mateus Roder and Gustavo de Rosa"
 
 # The short X.Y version
-version = "1.1.3"
+version = "1.1.4"
 
 # The full version, including alpha/beta/rc tags
-release = "1.1.3"
+release = "1.1.4"
 
 
 # -- General configuration ---------------------------------------------------

examples/applications/bernoulli/conv_rbm_classification.py

@@ -52,10 +52,10 @@
 nf = model.n_filters
 
 if model.maxpooling:
-    input_fc = nf * (h1//2 + 1) * (h2//2 + 1)
+    input_fc = nf * (h1 // 2 + 1) * (h2 // 2 + 1)
 else:
     input_fc = nf * h1 * h2
-fc = nn.Linear(input_fc , n_classes)
+fc = nn.Linear(input_fc, n_classes)
 
 # Check if model uses GPU
 if model.device == "cuda":
@@ -99,8 +99,7 @@
         y = model(x_batch)
 
         # Reshaping the outputs
-        y = y.reshape(
-            x_batch.size(0), input_fc)
+        y = y.reshape(x_batch.size(0), input_fc)
 
         # Calculating the fully-connected outputs
         y = fc(y)
@@ -131,8 +130,7 @@
         y = model(x_batch)
 
         # Reshaping the outputs
-        y = y.reshape(
-            x_batch.size(0), input_fc)
+        y = y.reshape(x_batch.size(0), input_fc)
 
         # Calculating the fully-connected outputs
         y = fc(y)

examples/applications/deep/conv_dbn_classification.py

@@ -1,10 +1,10 @@
 import torch
-import torchvision
 import torch.nn as nn
 import torch.optim as optim
-
+import torchvision
 from torch.utils.data import DataLoader
 from tqdm import tqdm
+
 from learnergy.models.deep import ConvDBN
 
 # Creating training and testing dataset
@@ -33,7 +33,7 @@
     momentum=(0, 0),
     decay=(0, 0),
     maxpooling=(True, False),
-    #pooling_kernel=(2, 0), # WORKING ON ...
+    # pooling_kernel=(2, 0), # WORKING ON ...
     use_gpu=True,
 )
 
@@ -46,22 +46,22 @@
 model.fit(train, batch_size=batch_size, epochs=epochs)
 
 # Reconstructing test set
-#rec_mse, v = model.reconstruct(test)
+# rec_mse, v = model.reconstruct(test)
 
 # Saving model
 torch.save(model, "model.pth")
 
 # Creating the Fully Connected layer to append on top of DBN
-h1 = model.models[len(model.models)-1].hidden_shape[0]
-h2 = model.models[len(model.models)-1].hidden_shape[1]
-nf = model.models[len(model.models)-1].n_filters
+h1 = model.models[len(model.models) - 1].hidden_shape[0]
+h2 = model.models[len(model.models) - 1].hidden_shape[1]
+nf = model.models[len(model.models) - 1].n_filters
 
-if model.models[len(model.models)-1].maxpooling:    
-    input_fc = nf * (h1//2 + 1) * (h2//2 + 1)
-    print('pooling', input_fc)
+if model.models[len(model.models) - 1].maxpooling:
+    input_fc = nf * (h1 // 2 + 1) * (h2 // 2 + 1)
+    print("pooling", input_fc)
 else:
     input_fc = nf * h1 * h2
-fc = nn.Linear(input_fc , n_classes)
+fc = nn.Linear(input_fc, n_classes)
 
 # Check if model uses GPU
 if model.device == "cuda":
@@ -101,10 +101,9 @@
 
         # Passing the batch down the model
         y = model(x_batch)
-        
+
         # Reshaping the outputs
-        y = y.reshape(
-            x_batch.size(0), input_fc)
+        y = y.reshape(x_batch.size(0), input_fc)
 
         # Calculating the fully-connected outputs
         y = fc(y)
@@ -133,10 +132,9 @@
 
         # Passing the batch down the model
         y = model(x_batch)
-        
+
         # Reshaping the outputs
-        y = y.reshape(
-            x_batch.size(0), input_fc)
+        y = y.reshape(x_batch.size(0), input_fc)
 
         # Calculating the fully-connected outputs
         y = fc(y)

examples/applications/gaussian/gaussian_conv_rbm_classification.py

@@ -52,10 +52,10 @@
 nf = model.n_filters
 
 if model.maxpooling:
-    input_fc = nf * (h1//2 + 1) * (h2//2 + 1)
+    input_fc = nf * (h1 // 2 + 1) * (h2 // 2 + 1)
 else:
     input_fc = nf * h1 * h2
-fc = nn.Linear(input_fc , n_classes)
+fc = nn.Linear(input_fc, n_classes)
 
 # Check if model uses GPU
 if model.device == "cuda":
@@ -99,8 +99,7 @@
         y = model(x_batch)
 
         # Reshaping the outputs
-        y = y.reshape(
-            x_batch.size(0), input_fc)
+        y = y.reshape(x_batch.size(0), input_fc)
 
         # Calculating the fully-connected outputs
         y = fc(y)
@@ -131,8 +130,7 @@
         y = model(x_batch)
 
         # Reshaping the outputs
-        y = y.reshape(
-            x_batch.size(0), input_fc)
+        y = y.reshape(x_batch.size(0), input_fc)
 
         # Calculating the fully-connected outputs
         y = fc(y)

learnergy/__init__.py

@@ -2,4 +2,4 @@
 of several modules and sub-modules.
 """
 
-__version__ = "1.1.3"
+__version__ = "1.1.4"

learnergy/core/dataset.py

@@ -2,7 +2,6 @@
 """
 
 from typing import Optional, Tuple
-from xmlrpc.client import Boolean
 
 import numpy as np
 import torch
@@ -17,15 +16,19 @@ class Dataset(torch.utils.data.Dataset):
     """A custom dataset class, inherited from PyTorch's dataset."""
 
     def __init__(
-        self, data: np.array, targets: np.array, 
-        transform: Optional[callable] = None, show_log: Optional[Boolean] = True
+        self,
+        data: np.array,
+        targets: np.array,
+        transform: Optional[callable] = None,
+        show_log: Optional[bool] = True,
     ) -> None:
         """Initialization method.
 
         Args:
             data: An n-dimensional array containing the data.
             targets: An 1-dimensional array containing the data's labels.
             transform: Optional transform to be applied over a sample.
+            show_log: Whether to show log information or not.
 
         """
 
@@ -83,7 +86,7 @@ def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
             idx: The idx of desired sample.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor]): Data and label tensors.
+            Data and label tensors.
 
         """
 
@@ -99,7 +102,7 @@ def __len__(self) -> int:
         """A private method that will be the base for PyTorch's iterator getting dataset's length.
 
         Returns:
-            (int): Length of dataset.
+            Length of dataset.
 
         """
 

learnergy/math/scale.py

@@ -13,7 +13,7 @@ def unitary_scale(x: np.array) -> np.array:
         x: A numpy array to be scaled.
 
     Returns:
-        (np.array): Scaled array.
+        Scaled array.
 
     """
 

learnergy/models/bernoulli/conv_rbm.py

@@ -55,7 +55,7 @@ def __init__(
             momentum: Momentum parameter.
             decay: Weight decay used for penalization.
             maxpooling: Whether MaxPooling2D should be used or not.
-            pooling_kernel: The kernel size of MaxPooling layer (when maxpooling=True).
+            pooling_kernel: The kernel size of MaxPooling2D layer (when maxpooling=True).
             use_gpu: Whether GPU should be used or not.
 
         """
@@ -80,7 +80,9 @@ def __init__(
         self.decay = decay
 
         if maxpooling:
-            self.maxpol2d = nn.MaxPool2d(kernel_size=pooling_kernel, stride=2, padding=1)
+            self.maxpol2d = nn.MaxPool2d(
+                kernel_size=pooling_kernel, stride=2, padding=1
+            )
             self.maxpooling = True
         else:
             self.maxpol2d = maxpooling
@@ -115,7 +117,8 @@ def __init__(
             self.momentum,
             self.decay,
             self.maxpooling,
-            pooling_kernel, pooling_kernel,
+            pooling_kernel,
+            pooling_kernel,
         )
 
     @property
@@ -233,7 +236,7 @@ def decay(self, decay: float) -> None:
 
     @property
     def maxpooling(self) -> bool:
-        """Usage of MaxPooling."""
+        """Usage of MaxPooling2D."""
 
         return self._maxpooling
 
@@ -291,7 +294,7 @@ def hidden_sampling(self, v: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
             v: A tensor incoming from the visible layer.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor]): The probabilities and states of the hidden layer sampling.
+            The probabilities and states of the hidden layer sampling.
 
         """
 
@@ -308,7 +311,7 @@ def visible_sampling(self, h: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
             h: A tensor incoming from the hidden layer.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor]): The probabilities and states of the visible layer sampling.
+            The probabilities and states of the visible layer sampling.
 
         """
 
@@ -327,7 +330,7 @@ def gibbs_sampling(
             v: A tensor incoming from the visible layer.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]): The probabilities and states of the hidden layer sampling (positive),
+            The probabilities and states of the hidden layer sampling (positive),
                 the probabilities and states of the hidden layer sampling (negative)
                 and the states of the visible layer sampling (negative).
 
@@ -362,7 +365,7 @@ def energy(self, samples: torch.Tensor) -> torch.Tensor:
             samples: Samples to be energy-freed.
 
         Returns:
-            (torch.Tensor): The system's energy based on input samples.
+            The system's energy based on input samples.
 
         """
 
@@ -392,7 +395,7 @@ def fit(
             epochs: Number of training epochs.
 
         Returns:
-            (float): MSE (mean squared error) from the training step.
+            MSE (mean squared error) from the training step.
 
         """
 
@@ -454,7 +457,7 @@ def reconstruct(
             dataset: A Dataset object containing the testing data.
 
         Returns:
-            (Tuple[float, torch.Tensor]): Reconstruction error and visible probabilities, i.e., P(v|h).
+            Reconstruction error and visible probabilities, i.e., P(v|h).
 
         """
 
@@ -498,7 +501,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             x: An input tensor for computing the forward pass.
 
         Returns:
-            (torch.Tensor): A tensor containing the Convolutional RBM's outputs.
+            A tensor containing the Convolutional RBM's outputs.
 
         """
 

learnergy/models/bernoulli/discriminative_rbm.py

@@ -132,7 +132,7 @@ def labels_sampling(self, samples: torch.Tensor) -> torch.Tensor:
             samples: Samples to be labels-calculated.
 
         Returns:
-            (torch.Tensor): Labels' probabilities based on input samples.
+            Labels' probabilities based on input samples.
 
         """
 
@@ -164,7 +164,7 @@ def fit(
             epochs: Number of training epochs.
 
         Returns:
-            (Tuple[float, float]): Loss and accuracy from the training step.
+            Loss and accuracy from the training step.
 
         """
 
@@ -223,7 +223,7 @@ def predict(
             dataset: A Dataset object containing the testing data.
 
         Returns:
-            (Tuple[float, torch.Tensor, torch.Tensor]): Accuracy, prediction probabilities and labels, i.e., P(y|v).
+            Accuracy, prediction probabilities and labels, i.e., P(y|v).
 
         """
 
@@ -334,7 +334,7 @@ def hidden_sampling(
             scale: A boolean to decide whether temperature should be used or not.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor]): The probabilities and states of the hidden layer sampling.
+            The probabilities and states of the hidden layer sampling.
 
         """
 
@@ -356,7 +356,7 @@ def class_sampling(self, h: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
             h: A tensor incoming from the hidden layer.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor]): The probabilities and states of the class layer sampling.
+            The probabilities and states of the class layer sampling.
 
         """
 
@@ -378,7 +378,7 @@ def gibbs_sampling(
             y: A tensor incoming from the class layer.
 
         Returns:
-            (Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]): The probabilities and states of the hidden layer sampling (positive),
+            The probabilities and states of the hidden layer sampling (positive),
                 the probabilities and states of the hidden layer sampling (negative)
                 and the states of the visible layer sampling (negative).
 
@@ -420,7 +420,7 @@ def fit(
             epochs: Number of training epochs.
 
         Returns:
-            (Tuple[float, float]): Loss and accuracy from the training step.
+            Loss and accuracy from the training step.
 
         """