asanAI

import numpy as np
import pandas as pd

def write_file_for_tfjs(name, data):
    with open(name + '.txt', 'w') as outfile:
        outfile.write('# shape: {0}\n'.format(data.shape))
        
        for data_slice in data.values:
            data_slice = np.array(data_slice, dtype=np.float64)
            
            np.savetxt(outfile, data_slice.reshape(1, -1), fmt='%.18e')
            outfile.write('# New slice\n')

# Load your data into your custom DataFrame
x_train = pd.DataFrame([1, 2, 3, 5, 6, 7])
y_train = pd.DataFrame([9, 8, 7, 6, 5, 4])

write_file_for_tfjs("x", x_train)
write_file_for_tfjs("y", y_train)

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FPS: 5 FPS

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🐍 Ctrl+Enter Ctrl+S Ctrl+/ Esc 13px

# 👋 Hello World!
# The simplest example to get started.

print("Hello, World! 🌍")
print("This is Python running in your browser via Pyodide!")
print()
print("Try editing this code and pressing ▶ Run (or Ctrl+Enter)")
print()
print("Available functions:")
print("  predict(data)           — Run model prediction")
print("  get_model_info()        — Get model architecture info")
print("  get_weights()           — Get model weights")
print("  rand_nested(shape)      — Generate random nested list")
print("  create_canvas(w, h)     — Create a drawable canvas")
print("  display(canvas)         — Show canvas in console")
print("  display_html(html)      — Render HTML in console")
print("  display_image(url)      — Show image in console")
print()
print("Pre-injected helpers (call or override):")
print("  _setup_model()          — Setup + return state dict")
print("  _print_model_summary()  — Print formatted model info")
print("  _predict_and_show(data) — Predict + display result")
print("  _format_result(result)  — Format result as string")
print("  _print_all_classes(r)   — Print sorted class bars")
print("  _get_label(idx)         — Get label for class index")
print("  _sample_shape()         — Get sample input shape")
print("  _draw_bar_chart(vals)   — Draw bar chart on canvas")
print("  _smooth_predictions(buf)— Average prediction buffer")
print("  _top_prediction(result) — Get (idx, conf) of top class")

▸

Feature Maps
Activation Function



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x =
x* =
W =
b =
f(\textbf{x}; \textbf{W}, b) =
\textbf{x}* = \mathrm{argmax}_xf(\textbf{x}; \textbf{W}, b)

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1. (model.json)
2. (labels.json)
3. (custom_images.zip)
43. (weights.json)



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