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| import streamlit as st | |
| from nn import NeuralNetwork | |
| import json | |
| from utils import sigmoid, sigmoid_prime | |
| INPUTS = [[0,0],[0,1],[1,0],[1,1]] | |
| OUTPUTS = [[0],[1],[1],[0]] | |
| def resetSession(): | |
| st.session_state.nn = None | |
| st.session_state.train_count = 0 | |
| ## Controller Function | |
| def runNN(): | |
| nn = st.session_state.nn | |
| df = { | |
| "input": [], | |
| "expected": [], | |
| "predicted": [], | |
| "rounded": [], | |
| "correct": [] | |
| } | |
| for i in range(4): | |
| result = nn.predict(INPUTS[i][0],INPUTS[i][1], activation=sigmoid) | |
| df["input"].append(f"{INPUTS[i][0]} xor {INPUTS[i][1]}") | |
| df["expected"].append(OUTPUTS[i][0]) | |
| df["predicted"].append(result) | |
| df["rounded"].append(round(result)) | |
| df["correct"].append('correct' if round(result)==OUTPUTS[i][0] else 'incorrect') | |
| st.dataframe(df) | |
| # st.write(f"for input `{INPUTS[i][0]} xor {INPUTS[i][1]}` expected `{OUTPUTS[i][0]}` predicted `{result}` which rounds to `{round(result)}` and is `{ 'correct' if round(result)==OUTPUTS[i][0] else 'incorrect' }`") | |
| def sidebar(): | |
| # Neural network controls | |
| st.sidebar.header('Neural Network Controls') | |
| st.sidebar.text('Number of epochs') | |
| epochs = st.sidebar.slider('Epochs', 1, 10000, 500) | |
| st.sidebar.text('Learning rate') | |
| alphas = st.sidebar.slider('Alphas', 1, 100, 20) | |
| col1, col2 = st.sidebar.columns(2) | |
| if col1.button('New Model'): | |
| btnNewModel() | |
| if col2.button('Reset Model'): | |
| resetSession() | |
| if "nn" in st.session_state and st.session_state.nn is not None: | |
| if st.sidebar.button('Train Model'): | |
| btnTrainModel(epochs, alphas) | |
| if st.sidebar.button('Run Neural Network'): | |
| btnRunModel() | |
| st.sidebar.download_button(label="Save Model", data=json.dumps(st.session_state.nn.getModelJson()), file_name="model.json", mime="application/json") | |
| def btnNewModel(): | |
| resetSession() | |
| st.session_state.nn = NeuralNetwork() | |
| st.sidebar.text("New model created") | |
| def btnTrainModel(epochs, alphas): | |
| st.session_state.nn.train(inputs=INPUTS, outputs=OUTPUTS, epochs=epochs, alpha=alphas) | |
| st.session_state.train_count += 1 | |
| st.sidebar.text(f"Model trained {st.session_state.train_count} times") | |
| def btnRunModel(): | |
| runNN() | |
| def btnResetModel(): | |
| resetSession() | |
| st.sidebar.text("Model reset") | |
| def app(): | |
| # initSession() | |
| st.title('Simple Neural Network App') | |
| st.write('This is the Neural Network image we are trying to implement!') | |
| st.image('nn.png', width=500) | |
| sidebar() | |
| st.markdown(''' | |
| ### References | |
| * https://www.codingame.com/playgrounds/59631/neural-network-xor-example-from-scratch-no-libs | |
| ''') | |
| if __name__ == '__main__': | |
| app() | |