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import tensorflowjs as tfjs
# creating and training of model using Keras
# ...
tfjs.converters.save_keras_model(model, './ModelJS')

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$ tensorflowjs_converter --input_format keras ./ModelPY/model.h5 ./../ModelJS

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import * as tf from '@tensorflow/tfjs';
// Definition of the component supporting the model
// ...
protected async loadModel() {
    this.model = await tf.loadModel(AppSettings.mnistModelUrl);
}

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protected async predict(imageData: ImageData) {

    const pred = await tf.tidy(() => {

      let img:any = tf.fromPixels(imageData, 1);
      img = img.reshape([1, 28, 28, 1]);
      img = tf.cast(img, 'float32');

      const output = this.model.predict(img) as any;

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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.8.0"> </script>

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model.add(Conv2D(
    filters = 32, 
    kernel_size = (5,5),
    padding = 'Same', 
    activation ='relu',
    input_shape = (28,28,1)
))

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from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(4, input_dim=2,activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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def create_frames(dt, steps, padding, output_dir):
    fig, ax = create_blank_chart_with_styling((6, 6))
    # creation of data describing the trajectory 
    xs, ys, zs = build_lorenz_trajectory(dt, steps)
    # setting the fixed range of axes
    ax.set_xlim3d(xs.min() - padding, xs.max() + padding)
    ax.set_ylim3d(ys.min() - padding, ys.max() + padding)
    ax.set_zlim3d(zs.min() - padding, zs.max() + padding)

    for i in range(steps-1):

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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib.animation import FuncAnimation

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# Calculation of the points belonging to the three trajectories, 
# based on the given starting conditions
plots_data = [build_lorenz_trajectory(DELTA_T, STEPS, 
    initial_values=initial_conditions[i]) for i in range(3)]

# Creation of an empty chart
fig, ax = create_blank_chart_with_styling(plots_data, PADDING, (8, 8))

# Setting up (for the time being empty) data sequences for each trajectory
plots = [ax.plot([],[],[], color=colors[i], label=str(initial_conditions[i]),