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| import streamlit as st | |
| import tensorflow | |
| from tensorflow import keras | |
| import seaborn as sns | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.datasets import make_blobs | |
| from sklearn.datasets import make_circles | |
| from sklearn.datasets import make_moons | |
| from keras.activations import relu,sigmoid,tanh | |
| from tensorflow.keras.optimizers import Adam, SGD, RMSprop, Adagrad, Nadam | |
| st.title("Implementation of Tensorflow Playground") | |
| dataset=st.selectbox('Choose a dataset type', | |
| options=['make_blobs', 'make_circles', 'make_moons']) | |
| if dataset=='make_blobs': | |
| X, y = make_blobs(n_samples=1000,random_state=42,centers=2, cluster_std=1.5) | |
| elif dataset =='make_circles': | |
| X, y = make_circles(n_samples=1000,random_state=42,noise=0.2,factor=0.5) | |
| else: | |
| X, y = make_moons(n_samples=1000, random_state=42,noise=0.2) | |
| training_Split_Ratio=st.number_input('Enter Train-Test Split Ratio',min_value=0.1,step=0.1) | |
| batch=st.number_input('Enter Batch Size',min_value=8,step=8) | |
| no_hidden_layer=st.number_input('Enter no.of hidden layers',min_value=1) | |
| layers=[] | |
| with st.expander("🔧 Configure Hidden Layers"): | |
| for i in range(1, no_hidden_layer + 1): | |
| st.markdown(f"**Hidden Layer {i}**") | |
| cols = st.columns(2) | |
| with cols[0]: | |
| neurons = st.number_input(f'Neurons in Layer {i}', min_value=5, step=1, key=f"neurons_{i}") | |
| with cols[1]: | |
| activation_fun = st.selectbox(f'Activation of Layer {i}', ['relu', 'sigmoid', 'tanh'], key=f"activation_{i}") | |
| layers.append((neurons, activation_fun)) | |
| optimizers = st.selectbox("Select Optimizer", ['adam', 'sgd', 'rmsprop', 'adagrad']) | |
| learning_rate = st.number_input('Enter Learning Rate', min_value=0.001, max_value=1.0, step=0.001, format="%.4f") | |
| epoch=st.number_input('Enter no.of epochs',min_value=15,step=1) | |
| if optimizers == 'adam': | |
| optimizer = Adam(learning_rate=learning_rate) | |
| elif optimizers == 'sgd': | |
| optimizer = SGD(learning_rate=learning_rate) | |
| elif optimizers == 'rmsprop': | |
| optimizer = RMSprop(learning_rate=learning_rate) | |
| elif optimizers == 'adagrad': | |
| optimizer = Adagrad(learning_rate=learning_rate) | |
| if st.button('Train Model'): | |
| model=keras.Sequential() | |
| model.add(keras.layers.Input(shape=(X.shape[1],))) | |
| for neurons, activation in layers: | |
| model.add(keras.layers.Dense(neurons, activation=activation)) | |
| if dataset=='make_blobs': | |
| model.add(keras.layers.Dense(3,activation="softmax")) | |
| model.compile(loss="sparse_categorical_crossentropy",optimizer=optimizer,metrics=['accuracy']) | |
| else: | |
| model.add(keras.layers.Dense(1,activation="sigmoid")) | |
| model.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=['accuracy']) | |
| history=model.fit(X,y,validation_split=training_Split_Ratio,epochs=epoch,batch_size = batch,verbose=0) | |
| st.success("Model training complete!") | |
| st.subheader("Decision Boundary") | |
| def plot_decision_boundary(model, X, y): | |
| x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 | |
| y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 | |
| 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()] | |
| probs = model.predict(grid) | |
| if probs.ndim > 1 and probs.shape[1] > 1: | |
| Z = np.argmax(probs, axis=1).reshape(xx.shape) | |
| else: | |
| Z = (probs > 0.5).astype(int).reshape(xx.shape) | |
| plt.figure(figsize=(6, 5)) | |
| plt.contourf(xx, yy, Z, alpha=0.5, cmap="coolwarm") | |
| plt.scatter(X[:, 0], X[:, 1], c=y, cmap="coolwarm", edgecolors='k') | |
| plt.xticks([]), plt.yticks([]) | |
| st.pyplot(plt) | |
| plt.close() | |
| plot_decision_boundary(model, X, y) | |
| st.subheader("Training vs Validation Loss") | |
| fig2, ax2 = plt.subplots() | |
| ax2.plot(history.history['loss'], label='Train Loss') | |
| ax2.plot(history.history['val_loss'], label='Validation Loss') | |
| ax2.set_title('Training vs Testing Loss') | |
| ax2.set_xlabel('Epoch') | |
| ax2.set_ylabel('Loss') | |
| ax2.legend() | |
| ax2.grid(True) | |
| st.pyplot(fig2) | |
| st.subheader('Metrics') | |
| st.write("✅ Training Accuracy :", f"{history.history['accuracy'][-1]:.4f}") | |
| st.write("🤖 Testing Accuracy :", f"{history.history['val_accuracy'][-1]:.4f}") | |