Delete 3_SimpleRegression.py

3_SimpleRegression.py

@@ -1,74 +0,0 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from matplotlib import pyplot as plt
-
-# Function to build the model
-def build_model(my_learning_rate):
-    model = tf.keras.models.Sequential()
-    model.add(tf.keras.layers.Dense(units=1, input_shape=(1,)))
-    model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=my_learning_rate),
-                  loss='mean_squared_error',
-                  metrics=[tf.keras.metrics.RootMeanSquaredError()])
-    return model
-
-# Function to train the model
-def train_model(model, feature, label, epochs, batch_size):
-    history = model.fit(x=feature, y=label, batch_size=batch_size, epochs=epochs)
-    trained_weight = model.get_weights()[0][0]
-    trained_bias = model.get_weights()[1]
-    epochs = history.epoch
-    hist = pd.DataFrame(history.history)
-    rmse = hist["root_mean_squared_error"]
-    return trained_weight, trained_bias, epochs, rmse
-
-# Function to plot the model
-def plot_the_model(trained_weight, trained_bias, feature, label):
-    plt.figure(figsize=(10, 6))
-    plt.xlabel('Feature')
-    plt.ylabel('Label')
-
-    # Plot the feature values vs. label values
-    plt.scatter(feature, label, c='b')
-
-    # Create a red line representing the model
-    x0 = 0
-    y0 = trained_bias
-    x1 = feature[-1]
-    y1 = trained_bias + (trained_weight * x1)
-    plt.plot([x0, x1], [y0, y1], c='r')
-
-    st.pyplot(plt)
-
-# Function to plot the loss curve
-def plot_the_loss_curve(epochs, rmse):
-    plt.figure(figsize=(10, 6))
-    plt.xlabel('Epoch')
-    plt.ylabel('Root Mean Squared Error')
-
-    plt.plot(epochs, rmse, label='Loss')
-    plt.legend()
-    plt.ylim([rmse.min()*0.97, rmse.max()])
-    st.pyplot(plt)
-
-# Define the dataset
-my_feature = np.array([1.0, 2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0, 12.0], dtype=float).reshape(-1, 1)
-my_label   = np.array([5.0, 8.8,  9.6, 14.2, 18.8, 19.5, 21.4, 26.8, 28.9, 32.0, 33.8, 38.2], dtype=float).reshape(-1, 1)
-
-# Streamlit interface
-st.title("Simple Linear Regression with Synthetic Data")
-
-learning_rate = st.sidebar.slider('Learning Rate', min_value=0.001, max_value=1.0, value=0.01, step=0.01)
-epochs = st.sidebar.slider('Epochs', min_value=1, max_value=1000, value=10, step=1)
-batch_size = st.sidebar.slider('Batch Size', min_value=1, max_value=len(my_feature), value=12, step=1)
-
-if st.sidebar.button('Run'):
-    my_model = build_model(learning_rate)
-    trained_weight, trained_bias, epochs, rmse = train_model(my_model, my_feature, my_label, epochs, batch_size)
-
-    st.subheader('Model Plot')
-    plot_the_model(trained_weight, trained_bias, my_feature, my_label)
-
-    st.subheader('Loss Curve')
-    plot_the_loss_curve(epochs, rmse)