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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, df, feature, label, epochs, batch_size):
history = model.fit(x=df[feature], y=df[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, df):
plt.figure(figsize=(10, 6))
plt.xlabel(feature)
plt.ylabel(label)
random_examples = df.sample(n=200)
plt.scatter(random_examples[feature], random_examples[label])
x0 = 0
y0 = trained_bias
x1 = random_examples[feature].max()
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)
# Load the dataset
@st.cache_data
def load_data():
url = "https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv"
df = pd.read_csv(url)
df["median_house_value"] /= 1000.0
return df
training_df = load_data()
# Streamlit interface
st.title("Simple Linear Regression with Real Data")
if st.checkbox('Show raw data'):
st.write(training_df.head())
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=30, step=1)
batch_size = st.sidebar.slider('Batch Size', min_value=1, max_value=len(training_df), value=30, step=1)
feature = st.sidebar.selectbox('Select Feature', training_df.columns)
label = 'median_house_value'
my_model = None # Initialize the model variable
if st.sidebar.button('Run'):
my_model = build_model(learning_rate)
weight, bias, epochs, rmse = train_model(my_model, training_df, feature, label, epochs, batch_size)
st.subheader('Model Plot')
plot_the_model(weight, bias, feature, label, training_df)
st.subheader('Loss Curve')
plot_the_loss_curve(epochs, rmse)
# Function to make predictions
def predict_house_values(n, feature, label):
batch = training_df[feature][10000:10000 + n]
predicted_values = my_model.predict_on_batch(x=batch)
st.write("feature label predicted")
st.write(" value value value")
st.write(" in thousand$ in thousand$")
st.write("--------------------------------------")
for i in range(n):
st.write("%5.0f %6.0f %15.0f" % (training_df[feature][10000 + i],
training_df[label][10000 + i],
predicted_values[i][0] ))
n_predictions = st.sidebar.slider('Number of Predictions', min_value=1, max_value=100, value=10)
if my_model is not None and st.sidebar.button('Predict'):
st.subheader('Predictions')
predict_house_values(n_predictions, feature, label)
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