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+# %% [markdown]
+# # Previous Class Definitions
+# The previously defined Layer_Dense, Activation_ReLU, and Activation_Softmax
+
+# %%
+# imports
+import numpy as np
+import nnfs
+from nnfs.datasets import spiral_data
+nnfs.init()
+
+# %%
+class Layer_Dense:
+    def __init__(self, n_inputs, n_neurons):
+        # Initialize the weights and biases
+        self.weights = 0.01 * np.random.randn(n_inputs, n_neurons)  # Normal distribution of weights
+        self.biases = np.zeros((1, n_neurons))
+
+    def forward(self, inputs):
+        # Calculate the output values from inputs, weights, and biases
+        self.output = np.dot(inputs, self.weights) + self.biases        # Weights are already transposed
+
+class Activation_ReLU:
+    def forward(self, inputs):
+        self.output = np.maximum(0, inputs)
+        
+class Activation_Softmax:
+    def forward(self, inputs):
+        # Get the unnormalized probabilities
+        # Subtract max from the row to prevent larger numbers
+        exp_values = np.exp(inputs - np.max(inputs, axis=1, keepdims=True))
+
+        # Normalize the probabilities with element wise division
+        probabilities = exp_values / np.sum(exp_values, axis=1,keepdims=True)
+        self.output = probabilities
+
+# %% [markdown]
+# # Forward Pass with No Loss Consideration
+# 2 input neural network with 2 layers of 3 neurons each. ReLU activation in the first layer with Softmax in the second layer to normalize the outputs.
+
+# %%
+# Create dataset
+X, y = spiral_data(samples=100, classes=3)
+# Create Dense layer with 2 input features and 3 output values
+dense1 = Layer_Dense(2, 3)
+# Create ReLU activation (to be used with Dense layer):
+activation1 = Activation_ReLU()
+# Create second Dense layer with 3 input features (as we take output
+# of previous layer here) and 3 output values
+dense2 = Layer_Dense(3, 3)
+# Create Softmax activation (to be used with Dense layer):
+activation2 = Activation_Softmax()
+
+# Make a forward pass of our training data through this layer
+dense1.forward(X)
+
+# Make a forward pass through activation function
+# it takes the output of first dense layer here
+activation1.forward(dense1.output)
+# Make a forward pass through second Dense layer
+# it takes outputs of activation function of first layer as inputs
+dense2.forward(activation1.output)
+# Make a forward pass through activation function
+# it takes the output of second dense layer here
+activation2.forward(dense2.output)
+# Let's see output of the first few samples:
+print(activation2.output[:5])
+
+# %% [markdown]
+# # Calculating Network Error with Categorical Cross Entropy Loss
+# loss = negative sum of the expected output * log(neural network output)
+# loss = - sum(expected_i * log(nn_output_i)) for all i in outputs
+# 
+# In the classification case, incorrect outputs do not end up mattering as the expected_i for the wrong class is 0.
+# 
+
+# %%
+nn_outputs = np.array([
+    [0.7, 0.1, 0.2],
+    [0.1, 0.5, 0.4],
+    [0.02, 0.9, 0.08]])
+class_targets = [0, 1, 1]
+losses = -np.log(nn_outputs[range(len(nn_outputs)), class_targets])
+print(f"Losses: {losses}")
+print(f"Average Loss: {np.average(losses)}")
+
+# %% [markdown]
+# ## Loss with One Hot Encoding
+# Classification typically has the expected output to be all zero except for the class the inputs belong too. This leads to simplfiying the cross entropy loss calculation.
+
+# %%
+true_output = np.array([
+    [1, 0, 0],
+    [0, 1, 0],
+    [0, 1, 0]
+])
+
+nn_output = np.array([
+    [0.7, 0.2, 0.1],
+    [0.1, 0.5, 0.4],
+    [0.02, 0.9, 0.08]
+])
+
+# Element by element multiplication "erases" the output terms corresponding with 0
+A = true_output*nn_output
+
+# Sum the columns (ie, sum every element in row 0, then row 1, etc) because each row is a batch of output
+B = np.sum(A, axis = 1)
+
+# Get the cross entropy loss
+C = -np.log(B)
+
+print(f"Losses: {C}")
+print(f"Average Loss: {np.mean(C)}")
+
+
+# %% [markdown]
+# ## Implementing the Loss Class
+
+# %%
+# Base class for Loss functions
+class Loss:
+    '''Calculates the data and regularization losses given
+    model output and ground truth values'''
+    def calculate(self, output, y):
+        sample_losses = self.forward(output, y)
+        data_loss = np.average(sample_losses)
+        return data_loss
+
+# %% [markdown]
+# ## Implementing the Categorical Cross Entropy Loss Class
+
+# %%
+class Loss_CategoricalCrossEntropy(Loss):
+    def forward(self, y_pred, y_true):
+        '''y_pred is the neural network output
+        y_true is the ideal output of the neural network'''
+        samples = len(y_pred)
+        # Bound the predicted values 
+        y_pred_clipped = np.clip(y_pred, 1e-7, 1-1e-7)
+        
+        if len(y_true.shape) == 1:     # Categorically labeled
+            correct_confidences = y_pred_clipped[range(samples), y_true]
+        elif len(y_true.shape) == 2:   # One hot encoded
+            correct_confidences = np.sum(y_pred_clipped*y_true, axis=1)
+
+        # Calculate the losses
+        negative_log_likelihoods = -np.log(correct_confidences)
+        return negative_log_likelihoods
+
+# %%
+nn_outputs = np.array([
+    [0.7, 0.1, 0.2],
+    [0.1, 0.5, 0.4],
+    [0.02, 0.9, 0.08]])
+class_targets = np.array([
+    [1, 0, 0],
+    [0, 1, 0],
+    [0, 1, 0]])
+
+loss_function = Loss_CategoricalCrossEntropy()
+losses = loss_function.calculate(nn_outputs, class_targets)
+print(f"Losses: {losses}")
+print(f"Average Loss: {np.average(losses)}")
+
+# %% [markdown]
+# # Introducing Accuracy
+# In the simple example, if the highest value in the outputs align with the correct classification, then that accuracy is 1. Even if it was 51% red and 49% blue, and the true output is red, it would be considered fully accurate.
+
+# %%
+nn_outputs = np.array([
+    [0.7, 0.1, 0.2],
+    [0.1, 0.5, 0.4],
+    [0.02, 0.9, 0.08]])
+class_targets = np.array([
+    [1, 0, 0],
+    [0, 1, 0],
+    [0, 1, 0]])
+
+# Calculate the losses
+loss_function = Loss_CategoricalCrossEntropy()
+losses = loss_function.calculate(nn_outputs, class_targets)
+print(f"Losses: {losses}")
+print(f"Average Loss: {np.average(losses)}")
+
+# Calculate the accuracy
+predictions = np.argmax(nn_outputs, axis=1)
+# If targets are one-hot encoded - convert them
+if len(class_targets.shape) == 2:
+    class_targets = np.argmax(class_targets, axis=1)
+# True evaluates to 1; False to 0
+accuracy = np.mean(predictions == class_targets)
+print(f"Accuracy: {accuracy}")
+
+