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k 2025-09-05 14:39:57 -04:00
parent ccc3fa3ed4
commit c74924ccea
2 changed files with 1 additions and 163 deletions
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.gitignore vendored
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/.ipynb_checkpoints/ /.ipynb_checkpoints/
/__pycache__/ /__pycache__/
/data/ /data/
/music.safetensors

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train.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "0",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import data\n",
"import show\n",
"import model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1",
"metadata": {},
"outputs": [],
"source": [
"x,y = data.detaset(data.load())\n",
"len(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"sample=x[420]\n",
"show.showSpec(sample)\n",
"show.playSpec(sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3",
"metadata": {},
"outputs": [],
"source": [
"from tinygrad import nn\n",
"gen = model.Gen()\n",
"optimizer = nn.optim.AdamW(nn.state.get_parameters(gen), lr=1e-4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4",
"metadata": {},
"outputs": [],
"source": [
"@TinyJit\n",
"def jit_step(X: Tensor, Y: Tensor,epoch) -> Tensor:\n",
" Tensor.training = True\n",
" optimizer.zero_grad()\n",
" _, loss = gen.__Lcall__(X,Y,epoch)\n",
" loss.backward()\n",
" optimizer.step()\n",
" return loss.realize()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"x_np, y_np = np.stack(x), np.stack(y)\n",
"x_np = np.expand_dims(x_np, axis=1)\n",
"y_np = np.expand_dims(y_np, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"size=len(x)\n",
"BACH_SIZE=32\n",
"eshape = (BACH_SIZE, 1, 128, 216)\n",
"\n",
"for e in range(0,12):\n",
" print(f\"\\n--- Starting Epoch {e} ---\\n\")\n",
" l=0\n",
" \n",
" permutation = np.random.permutation(size)\n",
" x_np = x_np[permutation]\n",
" y_np = y_np[permutation]\n",
" \n",
" for i in range(0,size,BACH_SIZE):\n",
" tx,ty=Tensor(x_np[i:i+BACH_SIZE]),Tensor(y_np[i:i+BACH_SIZE])\n",
" if(tx.shape != eshape or ty.shape != eshape):\n",
" continue\n",
" l+=jit_step(tx,ty,e).numpy()\n",
" \n",
" l /= (size/BACH_SIZE)\n",
" print(f\"loss of {l}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"from tinygrad.nn.state import safe_save, get_state_dict\n",
"safe_save(get_state_dict(gen),\"music.safetensors\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}