Update app.py

app.py

@@ -1,3 +1,206 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import io
+from sklearn.datasets import make_classification, make_regression, make_moons, make_circles
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from keras.models import Sequential
+from keras.layers import InputLayer, Dense
+from keras.regularizers import L1, L2
+from mlxtend.plotting import plot_decision_regions
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# Title
+st.sidebar.title('Tensorflow Playground')
+
+# Problem Type
+problem_type = st.sidebar.selectbox('Problem Type', ['Classification', 'Regression', 'Moons', 'Circles'])
+
+# Choose Datasets
+st.sidebar.title('Choose Dataset')
+
+# Datasets
+data_set = st.sidebar.selectbox('Datasets', [
+    '1.ushape.csv', '2.concerticcir1.csv', '3.concertriccir2.csv', 
+    '4.linearsep.csv', '5.outlier.csv', '6.overlap.csv', 
+    '7.xor.csv', '8.twospirals.csv', '9.random.csv'
+])
+
+# Learning Rate
+learning_rate = st.sidebar.selectbox('Learning Rate', [0.00001, 0.0001, 0.001, 0.01, 0.03, 0.1, 0.3, 1, 3, 10])
+
+# Activation
+activation_func = st.sidebar.selectbox('Activation', ['tanh', 'Sigmoid', 'linear', 'relu', 'softmax'])
+
+# Regularization Rate
+regularization_rate = st.sidebar.selectbox('Regularization Rate', [0.00001, 0.0001, 0.001, 0.01, 0.03, 0.1, 0.3, 1, 3, 10])
+
+# Regularization
+regularization = st.sidebar.selectbox('Regularization', ['None', 'L1', 'L2'])
+
+# Epochs
+epochs = st.sidebar.select_slider("Select number of Epochs", options=[i for i in range(1, 1001)])
+
+# Split Train/Test
+test_size = st.sidebar.slider("Test Size (%)", min_value=10, max_value=90, value=40, step=1) / 100
+
+# Hidden layers
+hidden_layers = st.sidebar.select_slider('Hidden Layers', options=[i for i in range(1, 51)])
+
+# Regularization configuration
+kernel_regularizer = None
+bias_regularizer = None
+if regularization == 'L1':
+    kernel_regularizer = L1(regularization_rate)
+    bias_regularizer = L1(regularization_rate)
+elif regularization == 'L2':
+    kernel_regularizer = L2(regularization_rate)
+    bias_regularizer = L2(regularization_rate)
+
+# Load Data
+file_path = f"C:\\Users\\VARSHINA\\Govardhan\\Machine_Learning_and_Deep_Learning\\Deep Learning\\Projects\\Streamlit\\Tensorflow_playground\\web_app\\data\\{data_set}"
+df = pd.read_csv(file_path)
+X = df.iloc[:, :2].values
+y = df.iloc[:, -1].values
+
+# Build the model
+model = Sequential()
+model.add(InputLayer(input_shape=(2,)))
+for i in range(1, hidden_layers + 1):
+    n = st.sidebar.text_input(f'No of Neurons in Layer {i}', '2')
+    try:
+        n = int(n)
+        model.add(Dense(units=n, activation=activation_func, use_bias=True, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer))
+    except ValueError:
+        st.error(f"Invalid input for the number of neurons in Layer {i}. Please enter an integer.")
+
+# Final layer configuration based on problem type
+if problem_type == 'Regression':
+    model.add(Dense(units=1, activation='linear', use_bias=True))
+else:
+    model.add(Dense(units=1, activation='sigmoid', use_bias=True))
+
+# Batch Size
+batch_size = st.sidebar.select_slider("Batch Size", options=[i for i in range(1, len(X)+1)])
+
+# Initialize session state
+if 'model' not in st.session_state:
+    st.session_state.model = None
+if 'history' not in st.session_state:
+    st.session_state.history = None
+if 'X_train' not in st.session_state:
+    st.session_state.X_train = None
+if 'y_train' not in st.session_state:
+    st.session_state.y_train = None
+
+if st.sidebar.button('Submit'):
+    # Data Generation for classification problems
+    if problem_type == 'Classification':
+        X, y = make_classification(n_samples=10000, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2, class_sep=2.5, random_state=10)
+    elif problem_type == 'Moons':
+        X, y = make_moons(n_samples=10000, noise=0.1, random_state=20)
+    elif problem_type == 'Circles':
+        X, y = make_circles(n_samples=10000, noise=0.05, random_state=20)
+    elif problem_type == 'Regression':
+        X, y = make_regression(n_samples=10000, n_features=2, n_informative=2, n_targets=1, noise=0.05, random_state=20)
+
+    # Data visualization
+    st.subheader("Visualization of Data Points with Class Labels")
+    fig, ax = plt.subplots(figsize=(8, 4))
+    sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, ax=ax)
+    st.pyplot(fig)
+
+    # Split train/test
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=20, stratify=y)
+
+    # Standardize
+    scaler = StandardScaler()
+    X_train = scaler.fit_transform(X_train)
+    X_test = scaler.transform(X_test)
+
+    # Save model and training data in session state
+    st.session_state.model = model
+    st.session_state.X_train = X_train
+    st.session_state.y_train = y_train
+    st.session_state.X_test = X_test
+    st.session_state.y_test = y_test
+
+    # Display model summary
+    buffer = io.StringIO()
+    model.summary(print_fn=lambda x: buffer.write(x + '\n'))
+    st.subheader("Model Summary:")
+    st.text(buffer.getvalue())
+    buffer.close()
+
+    # Compile and train the model
+    if problem_type == 'Regression':
+        model.compile(optimizer='sgd', loss='mse', metrics=['mae', 'mse'])
+    else:
+        model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
+    
+    st.session_state.history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1, validation_split=0.2)
+
+    # Plot loss and accuracy
+    fig, ax = plt.subplots(figsize=(8, 4))
+    ax.plot(range(1, epochs + 1), st.session_state.history.history['loss'], label='Train loss')
+    ax.plot(range(1, epochs + 1), st.session_state.history.history['val_loss'], label='Val loss')
+    ax.set_title("Training and Validation Loss Analysis")
+    ax.set_xlabel('Epochs')
+    ax.set_ylabel('Loss')
+    ax.legend()
+    st.pyplot(fig)
+
+    if problem_type != 'Regression':
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.plot(range(1, epochs + 1), st.session_state.history.history['accuracy'], label='Train Accuracy')
+        ax.plot(range(1, epochs + 1), st.session_state.history.history['val_accuracy'], label='Val Accuracy')
+        ax.set_title('Training and Validation Accuracy Analysis')
+        ax.set_xlabel('Epochs')
+        ax.set_ylabel('Accuracy')
+        ax.legend()
+        st.pyplot(fig)
+
+    # Plot decision surface
+    fig, ax = plt.subplots(figsize=(8, 4))
+    plot_decision_regions(X=st.session_state.X_train, y=st.session_state.y_train.astype(int), clf=st.session_state.model)
+    st.pyplot(fig)
+
+    fig, ax = plt.subplots(figsize=(8, 4))
+    plot_decision_regions(X=st.session_state.X_test, y=st.session_state.y_test.astype(int), clf=st.session_state.model)
+    st.pyplot(fig)
+
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+
+'''
 # importing required libraries
 import streamlit as st
 import pandas as pd
@@ -15,8 +218,7 @@ from keras.regularizers import L1, L2
 import mlxtend
 from mlxtend.plotting import plot_decision_regions
 import warnings
-warnings.filterwarnings("ignore") 
-
+warnings.filterwarnings("ignore")
 
 
 # Title
@@ -89,7 +291,7 @@ else:
 
 
 # Construct the file path
-file_path = rf"C:\Users\VARSHINA\Govardhan\Machine_Learning_and_Deep_Learning\Deep Learning\Projects\Streamlit\Tensorflow_playground\web_app\data\{data_set}"
+file_path = f"C:\Users\VARSHINA\Govardhan\Machine_Learning_and_Deep_Learning\Deep Learning\Projects\Streamlit\Tensorflow_playground\web_app\data\{data_set}"
 
 # loading the data
 df = pd.read_csv(file_path)
@@ -253,5 +455,5 @@ if st.sidebar.button('Submit'):
         fig, axs = plt.subplots(figsize = (8,4)) 
         plot_decision_regions(X = st.session_state.X_test, y = st.session_state.y_test.astype(int), clf = st.session_state.model)
         st.pyplot(fig)     
-        
+'''