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import streamlit as st
from sklearn.datasets import make_moons, make_circles, make_blobs
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import tensorflow
from tensorflow import keras
# from tensorflow.keras.models import Sequential
# from tensorflow.keras.layers import Dense
# from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
st.title("Neural Network Hyperparameters")
# Dataset selection
dataset = st.selectbox("Select Dataset", ["moons", "circles", "blobs"])
# Learning rate
learning_rate = st.number_input("Learning Rate", value=0.01, format="%.5f")
# Activation function
activation = st.selectbox("Activation Function", ["relu", "sigmoid", "tanh"])
# Train-test split
split_ratio = st.slider("Train-Test Split Ratio", min_value=0.1, max_value=0.9, value=0.8)
# Batch size
batch_size = st.number_input("Batch Size", min_value=1, value=32)
# Generate dataset
def generate_data(dataset):
if dataset == "moons":
return make_moons(n_samples=1000, noise=0.2, random_state=42)
elif dataset == "circles":
return make_circles(n_samples=1000, noise=0.2, factor=0.5, random_state=42)
elif dataset == "blobs":
return make_blobs(n_samples=1000, centers=2, random_state=42, cluster_std=1.5)
X, y = generate_data(dataset)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=(1 - split_ratio), random_state=42)
# Build model
model = keras.Sequential([
keras.layers.Dense(10, input_shape=(2,), activation=activation),
keras.layers.Dense(5, activation=activation),
keras.layers.Dense(1, activation="sigmoid") # binary classification
])
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
# Train model
history = model.fit(X_train, y_train, epochs=100, batch_size=batch_size,
validation_data=(X_test, y_test), verbose=0)
#4. Training vs Testing Error Plot
def plot_loss(history):
plt.figure(figsize=(8, 4))
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Test Loss')
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.title("Training vs Testing Loss")
st.pyplot(plt)
def plot_decision_boundary(model, X, y):
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 300),
np.linspace(y_min, y_max, 300))
grid = np.c_[xx.ravel(), yy.ravel()]
preds = model.predict(grid)
preds = preds.reshape(xx.shape)
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, preds, alpha=0.7, cmap=plt.cm.RdBu)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdBu, edgecolors='white')
plt.title("Decision Boundary")
st.pyplot(plt)
if st.button("Train Model"):
st.title("Neural Network Training Visualizer")
with st.spinner("Training the model..."):
# Call training functions
plot_loss(history)
plot_decision_boundary(model, X, y)
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