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Tensorflow_Playground / pages /Random_data.py

Update pages/Random_data.py

defe30d verified over 1 year ago

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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)

	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)