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import torch
import torch.nn as nn
import numpy as np
from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import networkx as nx
# Supported activations
ACTIVATION_MAP = {
'ReLU': nn.ReLU(),
'Tanh': nn.Tanh(),
'Sigmoid': nn.Sigmoid(),
'LeakyReLU': nn.LeakyReLU(),
'Identity': nn.Identity()
}
class MLP(nn.Module):
def __init__(self, input_size, hidden_sizes, output_size, activations):
super(MLP, self).__init__()
self.layers = nn.ModuleList()
self.activations = []
# Input layer
self.layers.append(nn.Linear(input_size, hidden_sizes[0]))
self.activations.append(ACTIVATION_MAP[activations[0]])
# Hidden layers
for i in range(len(hidden_sizes)-1):
self.layers.append(nn.Linear(hidden_sizes[i], hidden_sizes[i+1]))
self.activations.append(ACTIVATION_MAP[activations[i+1]])
# Output layer
self.layers.append(nn.Linear(hidden_sizes[-1], output_size))
self.activations.append(ACTIVATION_MAP['Identity']) # No activation for output
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
for i, layer in enumerate(self.layers[:-1]):
x = self.activations[i](layer(x))
x = self.layers[-1](x)
return self.softmax(x)
def generate_dataset(n_samples, n_features, n_classes, random_state=42):
X, y = make_classification(
n_samples=n_samples,
n_features=n_features,
n_classes=n_classes,
n_informative=n_features,
n_redundant=0,
random_state=random_state
)
# Scale the features
scaler = StandardScaler()
X = scaler.fit_transform(X)
return X, y
def split_data(X, y, val_pct, test_pct, random_state=42):
np.random.seed(random_state)
n = X.shape[0]
idx = np.random.permutation(n)
n_test = int(n * test_pct)
n_val = int(n * val_pct)
n_train = n - n_val - n_test
train_idx = idx[:n_train]
val_idx = idx[n_train:n_train+n_val]
test_idx = idx[n_train+n_val:]
return (X[train_idx], y[train_idx]), (X[val_idx], y[val_idx]), (X[test_idx], y[test_idx])
def train_model(model, X_train, y_train, X_val, y_val, epochs, learning_rate, batch_size=32, track_weights=False):
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
X_train_tensor = torch.FloatTensor(X_train)
y_train_tensor = torch.LongTensor(y_train)
X_val_tensor = torch.FloatTensor(X_val)
y_val_tensor = torch.LongTensor(y_val)
n_samples = X_train.shape[0]
train_losses = []
train_accuracies = []
val_losses = []
val_accuracies = []
weights_history = []
for epoch in range(epochs):
# Training phase
model.train()
indices = torch.randperm(n_samples)
X_shuffled = X_train_tensor[indices]
y_shuffled = y_train_tensor[indices]
epoch_train_loss = 0
train_correct = 0
# Mini-batch training
for i in range(0, n_samples, batch_size):
batch_X = X_shuffled[i:i+batch_size]
batch_y = y_shuffled[i:i+batch_size]
optimizer.zero_grad()
outputs = model(batch_X)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
epoch_train_loss += loss.item()
# Calculate training accuracy
_, predicted = torch.max(outputs.data, 1)
train_correct += (predicted == batch_y).sum().item()
# Calculate average training loss and accuracy
avg_train_loss = epoch_train_loss / (n_samples / batch_size)
train_accuracy = train_correct / n_samples
# Validation phase
model.eval()
with torch.no_grad():
val_outputs = model(X_val_tensor)
val_loss = criterion(val_outputs, y_val_tensor)
_, val_predicted = torch.max(val_outputs.data, 1)
val_correct = (val_predicted == y_val_tensor).sum().item()
val_accuracy = val_correct / len(y_val)
train_losses.append(avg_train_loss)
train_accuracies.append(train_accuracy)
val_losses.append(val_loss.item())
val_accuracies.append(val_accuracy)
if track_weights:
weights_history.append(model.layers[0].weight.detach().cpu().numpy().copy())
return (train_losses, train_accuracies, val_losses, val_accuracies, weights_history) if track_weights else (train_losses, train_accuracies, val_losses, val_accuracies)
def plot_training_history(train_losses, train_accuracies, val_losses, val_accuracies):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
# Plot losses
ax1.plot(train_losses, label='Training Loss')
ax1.plot(val_losses, label='Validation Loss')
ax1.set_title('Training and Validation Loss')
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss')
ax1.legend()
# Plot accuracies
ax2.plot(train_accuracies, label='Training Accuracy')
ax2.plot(val_accuracies, label='Validation Accuracy')
ax2.set_title('Training and Validation Accuracy')
ax2.set_xlabel('Epoch')
ax2.set_ylabel('Accuracy')
ax2.legend()
plt.tight_layout()
return fig
def plot_confusion_matrix(y_true, y_pred, n_classes):
from sklearn.metrics import confusion_matrix
import seaborn as sns
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=[f'Class {i}' for i in range(n_classes)],
yticklabels=[f'Class {i}' for i in range(n_classes)])
plt.title('Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('True')
plt.tight_layout()
return plt.gcf()
def plot_classification_metrics(y_true, y_pred, n_classes):
from sklearn.metrics import classification_report
import pandas as pd
report = classification_report(y_true, y_pred, output_dict=True)
df = pd.DataFrame(report).transpose()
df = df.drop('support', axis=1)
df = df.round(3)
return df
def visualize_weights(model):
weights = []
for layer in model.layers:
weights.append(layer.weight.detach().numpy())
n_layers = len(weights)
fig, axes = plt.subplots(1, n_layers, figsize=(5*n_layers, 5))
if n_layers == 1:
axes = [axes]
for i, (weight, ax) in enumerate(zip(weights, axes)):
im = ax.imshow(weight, cmap='coolwarm')
ax.set_title(f'Layer {i+1} Weights')
plt.colorbar(im, ax=ax)
plt.tight_layout()
return fig
def plot_weight_optimization(weights_history):
# Visualize the change of the first weight in the first neuron over epochs
weights_history = np.array(weights_history)
fig, ax = plt.subplots(figsize=(8, 4))
for i in range(weights_history.shape[1]):
ax.plot(weights_history[:, i, 0], label=f'Neuron {i+1}')
ax.set_title('First Layer Weights Optimization (first input weight per neuron)')
ax.set_xlabel('Epoch')
ax.set_ylabel('Weight Value')
ax.legend()
plt.tight_layout()
return fig
def visualize_network(input_size, hidden_sizes, output_size):
G = nx.DiGraph()
layers = [input_size] + hidden_sizes + [output_size]
pos = {}
node_labels = {}
node_count = 0
y_gap = 1.5
x_gap = 2
for l, n_nodes in enumerate(layers):
for n in range(n_nodes):
node_id = f'L{l}N{n}'
G.add_node(node_id, layer=l)
pos[node_id] = (l * x_gap, -n * y_gap + (n_nodes-1)*y_gap/2)
if l == 0:
node_labels[node_id] = f'In{n+1}'
elif l == len(layers)-1:
node_labels[node_id] = f'Out{n+1}'
else:
node_labels[node_id] = f'H{l}-{n+1}'
# Add edges
for l in range(len(layers)-1):
for n1 in range(layers[l]):
for n2 in range(layers[l+1]):
G.add_edge(f'L{l}N{n1}', f'L{l+1}N{n2}')
fig, ax = plt.subplots(figsize=(2*len(layers), 6))
nx.draw(G, pos, ax=ax, with_labels=True, labels=node_labels, node_size=1000, node_color='skyblue', arrowsize=10)
ax.set_title('MLP Architecture')
plt.tight_layout()
return fig |