| |
| import streamlit as st |
| import tensorflow as tf |
| import numpy as np |
| from tensorflow.keras.utils import plot_model |
| import matplotlib.pyplot as plt |
| from io import BytesIO |
|
|
| st.title("TensorFlow Neural Network Playground") |
|
|
| st.sidebar.header("Network Configuration") |
| input_nodes = st.sidebar.slider("Input Layer Nodes", 2, 10, 4) |
| hidden_nodes = st.sidebar.slider("Hidden Layer Nodes", 2, 10, 6) |
| output_nodes = st.sidebar.slider("Output Layer Nodes", 1, 5, 2) |
| learning_rate = st.sidebar.slider("Learning Rate", 0.01, 1.0, 0.1) |
|
|
| def create_model(input_dim, hidden_dim, output_dim, lr): |
| model = tf.keras.Sequential([ |
| tf.keras.layers.Dense(hidden_dim, activation='relu', input_shape=(input_dim,), name='Hidden_Layer'), |
| tf.keras.layers.Dense(output_dim, activation='softmax', name='Output_Layer') |
| ]) |
| model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr), |
| loss='categorical_crossentropy', |
| metrics=['accuracy']) |
| return model |
|
|
| def plot_network(model): |
| img_data = BytesIO() |
| plot_model(model, |
| to_file=img_data, |
| show_shapes=True, |
| show_layer_names=True, |
| rankdir='TB', |
| expand_nested=True, |
| dpi=96) |
| img_data.seek(0) |
| st.image(img_data.getvalue(), caption="Neural Network Architecture") |
|
|
| model = create_model(input_nodes, hidden_nodes, output_nodes, learning_rate) |
| st.subheader("Network Architecture") |
| plot_network(model) |
|
|
| def generate_sample_data(samples=100): |
| X = np.random.random((samples, input_nodes)) |
| y = np.random.randint(0, output_nodes, size=(samples,)) |
| y = tf.keras.utils.to_categorical(y, output_nodes) |
| return X, y |
|
|
| if st.button("Train Model"): |
| X, y = generate_sample_data() |
| history = model.fit(X, y, epochs=10, verbose=0) |
| st.write("Training Complete!") |
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) |
| ax1.plot(history.history['accuracy']) |
| ax1.set_title('Model Accuracy') |
| ax1.set_ylabel('Accuracy') |
| ax1.set_xlabel('Epoch') |
| ax2.plot(history.history['loss']) |
| ax2.set_title('Model Loss') |
| ax2.set_ylabel('Loss') |
| ax2.set_xlabel('Epoch') |
| st.pyplot(fig) |
|
|
| if st.checkbox("Show Model Summary"): |
| st.subheader("Model Summary") |
| summary_str = [] |
| model.summary(print_fn=lambda x: summary_str.append(x)) |
| st.markdown("### Model: sequential") |
| st.markdown(""" |
| | Layer (type) | Output Shape | Param # | |
| |----------------------|----------------------|---------|""") |
| for line in summary_str[1:-2]: |
| if 'dense' in line.lower(): |
| parts = line.split() |
| layer_name = parts[0] + " (Dense)" |
| output_shape = parts[1] |
| param_count = parts[2] |
| st.markdown(f"| {layer_name:<20} | {output_shape:<20} | {param_count:>7} |") |
| total_params = summary_str[-1].split()[-1] |
| st.markdown(f"**Total params:** {total_params}") |
