Spaces:
Runtime error
Runtime error
| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Sun Mar 26 21:07:00 2023 | |
| @author: Bernd Ebenhoch | |
| """ | |
| import tensorflow as tf | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from matplotlib import animation | |
| from matplotlib.animation import FuncAnimation | |
| import matplotlib as mpl | |
| import streamlit as st | |
| from matplotlib import cm | |
| import matplotlib.pyplot as plt | |
| from sklearn.linear_model import LinearRegression | |
| import mpl_toolkits.mplot3d as a3 | |
| import matplotlib.colors as colors | |
| from matplotlib.colors import LightSource | |
| from tensorflow import keras | |
| import pandas as pd | |
| # Farben definieren | |
| cb = [15/255, 25/255, 35/255] | |
| cf = [25/255*2, 35/255*2, 45/255*2] | |
| w = [242/255, 242/255, 242/255] | |
| blue = [68/255, 114/255, 196/255] | |
| orange = [197/255, 90/255, 17/255] | |
| tab1, tab2, tab3 = st.tabs(["Künstliche Neuronale Netze", "Wörter Maskieren", "Demos"]) | |
| with tab1: | |
| col1, col2 = tab1.columns(2) | |
| size = np.array([12., 27., 32., 47., 58., 56., 58., 61., | |
| 64., 67., 70., 80., 84., 88., 108.]) | |
| price = np.array([88., 135., 178., 216., 220., 246., 241., 275., | |
| 305., 267., 297., 310., 292., 317., 422.]) | |
| location = np.array([2., 2., 0., 1., 2., 0., 1., 0., 1., 2., 0., 2., 1., 1., 2.]) | |
| price[location == 1] = price[location == 1]*1+30 | |
| price[location == 2] = price[location == 2]*1+60 | |
| size_location = np.concatenate((size.reshape(-1, 1), location.reshape(-1, 1)), axis=1) | |
| data = np.concatenate((size.reshape(-1, 1), location.reshape(-1, 1), | |
| price.reshape(-1, 1)), axis=1) | |
| data = pd.DataFrame(data, columns=['Wohnungsgröße (qm)', 'Ort', 'Preis (k€)']) | |
| col1.dataframe(data.style.format(precision=0)) | |
| #edited_df = st.experimental_data_editor(data) | |
| edited_df = data | |
| edited_data = edited_df.to_numpy() | |
| size_location = edited_data[:, :2] | |
| price = edited_data[:, 2] | |
| string = col2.text_area( | |
| 'Architektur des neuronalen Netzes. Anzahl der Neuronen in den einzelnen Schichten', value='4', height=275) | |
| layers = string.split('\n') | |
| if st.button('Modell trainieren und Fit-Kurve darstellen'): | |
| with st.spinner('Der Fit-Prozess kann einige Sekunden dauern ...'): | |
| model = keras.models.Sequential() | |
| if len(layers) > 0: | |
| for neurons in layers: | |
| model.add(keras.layers.Dense(int(neurons), activation='tanh')) | |
| model.add(keras.layers.Dense(1, activation='tanh')) | |
| model.compile(loss='binary_crossentropy', optimizer='SGD') | |
| lr_reduction = keras.callbacks.ReduceLROnPlateau( | |
| monitor='loss', patience=1000, min_lr=0.00001) | |
| model.fit(size_location/[120, 2], price/500, epochs=5000, | |
| batch_size=4, callbacks=lr_reduction, verbose=False) | |
| y_pred = model.predict((size_location)/[120, 2], verbose=False).reshape(-1)*500 | |
| x = np.linspace(0, 125, 400) | |
| y = np.linspace(0, 2, 400) | |
| X, Y = np.meshgrid(x, y) | |
| Z = np.concatenate([X.reshape(-1, 1)/120, Y.reshape(-1, 1)/2], axis=1) | |
| Z = model.predict(Z, verbose=False)*500 | |
| Z = Z.reshape(len(y), len(x)) | |
| fig = plt.figure(facecolor=cb, figsize=(7, 7)) | |
| ax = fig.add_subplot(projection='3d') | |
| ax.tick_params(color=w, labelcolor=w, labelsize=12) | |
| ax.set_facecolor(cb) | |
| ax.w_xaxis.set_pane_color(cf) | |
| ax.w_yaxis.set_pane_color(cf) | |
| ax.w_zaxis.set_pane_color(cf) | |
| ax.set_yticks([0, 1, 2]) | |
| ax.view_init(25, 50) | |
| rgb = np.tile(orange, (Z.shape[0], Z.shape[1], 1)) | |
| ls = LightSource(azdeg=315, altdeg=45, hsv_min_val=0.9, | |
| hsv_max_val=1, hsv_min_sat=1, hsv_max_sat=0) | |
| illuminated_surface = ls.shade_rgb(rgb, Z) | |
| below_price = price[price < y_pred] | |
| below_location = location[price < y_pred] | |
| below_size = size[price < y_pred] | |
| ax.plot(below_size, below_location, below_price, '.', markersize=20, color=blue) | |
| ax.plot_surface(X, Y, Z, facecolors=illuminated_surface, edgecolors=[0, 0, 0, 0], | |
| linewidth=0, antialiased=True, rcount=400, ccount=400, alpha=0.8) | |
| above_price = price[price >= y_pred] | |
| above_location = location[price >= y_pred] | |
| above_size = size[price >= y_pred] | |
| ax.plot(above_size, above_location, above_price, | |
| '.', markersize=20, color=blue, zorder=20,) | |
| ax.set_ylim(2, 0) | |
| ax.set_xlim(125, 0) | |
| ax.set_zlim(0, 450) | |
| ax.set_xlabel('Wohnungsgröße (qm)', color=w, fontsize=15, labelpad=10) | |
| ax.set_ylabel('Ort', color=w, fontsize=15, labelpad=10) | |
| ax.set_zlabel('Preis (k€)', color=w, fontsize=15, rotation=270, labelpad=10) | |
| st.pyplot(fig) | |
| # %% | |
| with tab2: | |
| text_input = 'Das schöne Allgäu\n' + \ | |
| 'Das wunderbare Allgäu\n' + \ | |
| 'Das grüne Allgäu\n' + \ | |
| 'Radfahren im Allgäu\n' + \ | |
| 'Wandern im Allgäu\n' + \ | |
| 'Radfahren in Oberschwaben\n' + \ | |
| 'Urlaub in Oberschwaben\n' + \ | |
| 'Künstliche Intelligenz für das Allgäu\n' + \ | |
| 'Künstliche Intelligenz für Oberschwaben\n' + \ | |
| 'Data Science für Oberschwaben\n' + \ | |
| 'Data Science und Machine Learning\n' + \ | |
| 'Machine Learning für das Allgäu' | |
| string = st.text_area('', value=text_input, height=275) | |
| text = string.split('\n') | |
| if st.button('Modell trainieren und Wort-Vektoren darstellen'): | |
| with st.spinner('Der Fit-Prozess kann einige Sekunden dauern ...'): | |
| vectorizer = tf.keras.layers.TextVectorization( | |
| max_tokens=1000, output_sequence_length=7) | |
| vectorizer.adapt(text) | |
| def generator(): | |
| while True: | |
| x = vectorizer(text) | |
| mask = tf.reduce_max(x)+1 | |
| lengths = tf.argmin(x, axis=1) | |
| lengths = tf.cast(lengths, tf.float32) | |
| masks = tf.random.uniform(shape=(x.shape[0],), minval=0, maxval=lengths) | |
| masks = tf.cast(masks, tf.int32) | |
| masks = tf.one_hot(masks, x.shape[1], dtype=tf.int32) | |
| masks = tf.cast(masks, tf.bool) | |
| y = x[masks] | |
| masks = tf.cast(masks, tf.int64) | |
| x = x * (1-masks) + mask * masks | |
| yield x, y | |
| # data = tf.data.Dataset.from_tensor_slices(vectorizer(text),vectorizer(text)) | |
| # data = data.map(masking_generator) | |
| model = tf.keras.models.Sequential() | |
| model.add(tf.keras.layers.Embedding(vectorizer.vocabulary_size()+1, 3)) | |
| model.add(tf.keras.layers.LSTM(100, return_sequences=False, activation='sigmoid')) | |
| model.add(tf.keras.layers.Dense(vectorizer.vocabulary_size(), activation='softmax')) | |
| model.summary() | |
| model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', | |
| metrics=['accuracy']) | |
| lr_reduce = tf.keras.callbacks.ReduceLROnPlateau( | |
| monitor='loss', patience=500, min_lr=1e-6) | |
| model.fit(generator(), steps_per_epoch=1, | |
| epochs=3000, callbacks=lr_reduce, verbose=False) | |
| fig = plt.figure(facecolor=cb, figsize=(7, 7)) | |
| ax = fig.add_subplot() | |
| ax.tick_params(color=w, labelcolor=w, labelsize=12) | |
| ax.set_facecolor(cb) | |
| embed_model = tf.keras.models.Model(model.input, model.layers[0].output) | |
| X_embed = embed_model(vectorizer(vectorizer.get_vocabulary( | |
| include_special_tokens=False)))[:, 0, :] | |
| # 1. Dimension der Wort-Vektoren auf X-Achse, | |
| # 2. Dimension auf y-Achse, 3. auf die Z-Achse abbilden | |
| ax.scatter(X_embed[:, 0], X_embed[:, 1], | |
| color=blue) | |
| for i in range(vectorizer.vocabulary_size()-2): | |
| ax.text(X_embed[i, 0], X_embed[i, 1], | |
| vectorizer.get_vocabulary(include_special_tokens=False)[i], | |
| color=w) | |
| ax.set_ylim(-2, 2) | |
| ax.set_xlim(-2, 2) | |
| ax.set_xticks([-2, -1, 0, 1, 2]) | |
| ax.set_yticks([-2, -1, 0, 1, 2]) | |
| ax.spines['bottom'].set_color(w) | |
| ax.spines['top'].set_color(w) | |
| ax.spines['right'].set_color(w) | |
| ax.spines['left'].set_color(w) | |
| ax.set_xlabel('Dimension 1', color=w, fontsize=15, labelpad=10) | |
| ax.set_ylabel('Dimension 2', color=w, fontsize=15, labelpad=10) | |
| st.pyplot(fig) | |
| # %% | |
| with tab3: | |
| st.header("An owl") | |