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baf1078 1a72faa baf1078 785f1e8 baf1078 defe30d 785f1e8 baf1078 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | import streamlit as st
import base64
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_circles, make_moons, make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from mlxtend.plotting import plot_decision_regions
import numpy as np
import tensorflow as tf
# Page title with new theme
st.markdown(
"<h1 style='text-align: center; color: #FF6347;'>🤖 Neural Network Playground</h1>",
unsafe_allow_html=True
)
# Load and encode background image
def get_base64(file_path):
with open(file_path, "rb") as f:
data = f.read()
return base64.b64encode(data).decode()
img_base64 = get_base64("neuron.webp") # Make sure this image is in the same folder
# Inject CSS with base64 background
st.markdown(
f"""
<style>
.stApp {{
background-image: url("data:image/jpg;base64,{img_base64}");
background-size: cover;
background-position: center;
background-repeat: no-repeat;
background-attachment: fixed;
}}
</style>
""",
unsafe_allow_html=True
)
# Sidebar configuration with new theme
st.sidebar.title("⚙️ Model Configuration")
# User input options in sidebar with theme
num_points = st.sidebar.slider("Number of Data Points", 100, 10000, 1000, step=100)
noise = st.sidebar.slider("Noise", 0.01, 0.9, 0.1)
batch_size = st.sidebar.slider("Batch Size", 1, 512, 32)
epochs = st.sidebar.slider("Epochs", 1, 100, 10)
learning_rate = st.sidebar.slider("Learning Rate", 0.0001, 1.0, 0.01, step=0.0001, format="%.4f")
hidden_layers = st.sidebar.slider("Hidden Layers", 1, 5, 2)
neurons_per_layer = st.sidebar.slider("Neurons per Layer", 1, 512, 32)
activation_name = st.sidebar.selectbox("Activation Function", ["relu", "tanh", "sigmoid", "linear"])
# Dataset selection with new theme
st.subheader("📊 Dataset Selection")
dataset_option = st.selectbox("Choose the dataset", ("circle", "moons", "classification"))
# Dataset generation based on user selection
if dataset_option == "circle":
x, y = make_circles(n_samples=num_points, noise=noise, factor=0.5, random_state=42)
elif dataset_option == "moons":
x, y = make_moons(n_samples=num_points, noise=noise, random_state=42)
else:
x, y = make_classification(n_samples=num_points, n_features=2, n_informative=2,
n_redundant=0, n_clusters_per_class=1, random_state=42)
# Submit button
if st.button("🚀 Submit"):
st.subheader("📍 Input Data")
fig, ax = plt.subplots()
sns.scatterplot(x=x[:, 0], y=x[:, 1], hue=y, palette='Set2', ax=ax)
st.pyplot(fig)
# Train button with a fresh theme for model training
if st.button("🧠 Train the model"):
with st.spinner("⏳ Training the model..."):
# Data split and scale
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1, stratify=y)
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
# Model architecture
model = Sequential()
model.add(Dense(neurons_per_layer, input_shape=(2,), activation=activation_name))
for _ in range(hidden_layers - 1):
model.add(Dense(neurons_per_layer, activation=activation_name))
model.add(Dense(1, activation='sigmoid'))
# Compile and train
sgd = SGD(learning_rate=learning_rate)
model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=0)
st.success("✅ Training Complete!")
# Show training plots with a fresh look
st.subheader("📈 Training Progress")
fig, ax = plt.subplots()
ax.plot(history.history['loss'], label='Training Loss')
ax.plot(history.history['val_loss'], label='Validation Loss')
ax.set_title("Training vs Validation Loss")
ax.set_xlabel("Epoch")
ax.legend()
st.pyplot(fig)
# Display final loss metrics
final_loss = history.history['loss'][-1]
final_val_loss = history.history['val_loss'][-1]
st.write(f"🧮 Final Training Loss: **{final_loss:.4f}**")
st.write(f"✅ Final Validation Loss: **{final_val_loss:.4f}**")
# Decision boundary visualization with a fresh UI
class KerasClassifierWrapper:
def __init__(self, model):
self.model = model
def predict(self, X):
return (self.model.predict(X) > 0.5).astype("int32")
with st.spinner("🔮 Generating decision boundary..."):
st.subheader("📌 Decision Boundary (Training Data)")
fig, ax = plt.subplots()
plot_decision_regions(X=x_train, y=y_train, clf=KerasClassifierWrapper(model), ax=ax)
st.pyplot(fig) |