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Add SuperSimpleNorm and update synthetic env

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D-X-Y
2021-04-23 02:12:11 -07:00
parent a5b7d986b3
commit 9b895bdf2e
13 changed files with 238 additions and 519 deletions
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107
notebooks/TOT/synthetic-env.ipynb
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@@ -107,113 +107,6 @@
"visualize_env()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "supreme-basis",
"metadata": {},
"outputs": [],
"source": [
"# def optimize_fn(xs, ys, test_sets):\n",
"# xs = torch.FloatTensor(xs).view(-1, 1)\n",
"# ys = torch.FloatTensor(ys).view(-1, 1)\n",
" \n",
"# model = SuperSequential(\n",
"# SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
"# SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
"# )\n",
"# optimizer = torch.optim.Adam(\n",
"# model.parameters(),\n",
"# lr=0.01, weight_decay=1e-4, amsgrad=True\n",
"# )\n",
"# for _iter in range(100):\n",
"# preds = model(ys)\n",
"\n",
"# optimizer.zero_grad()\n",
"# loss = torch.nn.functional.mse_loss(preds, ys)\n",
"# loss.backward()\n",
"# optimizer.step()\n",
" \n",
"# with torch.no_grad():\n",
"# answers = []\n",
"# for test_set in test_sets:\n",
"# test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
"# preds = model(test_set).view(-1).numpy()\n",
"# answers.append(preds.tolist())\n",
"# return answers\n",
"\n",
"# def f(x):\n",
"# return np.cos( 0.5 * x + x * x)\n",
"\n",
"# def get_data(mode):\n",
"# dataset = SynAdaptiveEnv(mode=mode)\n",
"# times, xs, ys = [], [], []\n",
"# for i, (_, t, x) in enumerate(dataset):\n",
"# times.append(t)\n",
"# xs.append(x)\n",
"# dataset.set_transform(f)\n",
"# for i, (_, _, y) in enumerate(dataset):\n",
"# ys.append(y)\n",
"# return times, xs, ys\n",
"\n",
"# def visualize_syn(save_path):\n",
"# save_dir = (save_path / '..').resolve()\n",
"# save_dir.mkdir(parents=True, exist_ok=True)\n",
" \n",
"# dpi, width, height = 40, 2000, 900\n",
"# figsize = width / float(dpi), height / float(dpi)\n",
"# LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
" \n",
"# fig = plt.figure(figsize=figsize)\n",
" \n",
"# times, xs, ys = get_data(None)\n",
" \n",
"# def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
"# alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
"# if legend is not None:\n",
"# cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
"# cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
"# if not plot_only:\n",
"# cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
"# cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
"# for tick in cur_ax.xaxis.get_major_ticks():\n",
"# tick.label.set_fontsize(LabelSize - font_gap)\n",
"# tick.label.set_rotation(10)\n",
"# for tick in cur_ax.yaxis.get_major_ticks():\n",
"# tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
"# cur_ax = fig.add_subplot(2, 1, 1)\n",
"# draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
"\n",
"# cur_ax = fig.add_subplot(2, 1, 2)\n",
"# draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
" \n",
"# train_times, train_xs, train_ys = get_data(\"train\")\n",
"# draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
" \n",
"# valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
"# draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
" \n",
"# test_times, test_xs, test_ys = get_data(\"test\")\n",
"# draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
" \n",
"# # optimize MLP models\n",
"# # [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
"# # draw_ax(cur_ax, train_times, train_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
"# # import pdb; pdb.set_trace()\n",
"# # draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
"# # draw_ax(cur_ax, test_times, test_preds, None, None,\n",
"# # alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
"\n",
"# plt.legend(loc=1, fontsize=LegendFontsize)\n",
"\n",
"# fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
"# plt.close(\"all\")\n",
"# # plt.show()"
]
}
],
"metadata": {
"kernelspec": {