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@@ -2,7 +2,6 @@
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"cells": [
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{
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"cell_type": "markdown",
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- "metadata": {},
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"source": [
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"# Assignment 4\n",
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"\n",
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@@ -10,65 +9,70 @@
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"- **Zhilong Li**\n",
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"- **USC ID:** 3884-8441-02\n",
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"- **USC email:** zhilongl@usc.edu"
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- ]
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+ ],
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+ "metadata": {}
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},
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{
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"cell_type": "code",
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- "execution_count": 1,
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- "metadata": {
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- "ExecuteTime": {
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- "end_time": "2021-10-02T03:23:56.486153Z",
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- "start_time": "2021-10-02T03:23:54.993987Z"
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- }
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- },
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Init Plugin\n",
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- "Init Graph Optimizer\n",
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- "Init Kernel\n"
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- ]
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- }
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- ],
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+ "execution_count": null,
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"source": [
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"from tensorflow import keras\n",
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"import tensorflow as tf\n",
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- "import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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+ "import numpy as np\n",
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+ "\n",
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"import pickle\n",
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"plt.style.use('ggplot')"
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- ]
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+ ],
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+ "outputs": [
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+ {
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+ "output_type": "error",
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+ "ename": "Error",
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+ "evalue": "Kernel is dead",
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+ "traceback": [
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+ "Error: Kernel is dead",
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+ "at f._sendKernelShellControl (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/node_modules/@jupyterlab/services.js:3:428116)",
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+ "at f.sendShellMessage (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/node_modules/@jupyterlab/services.js:3:427885)",
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+ "at f.requestExecute (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/node_modules/@jupyterlab/services.js:3:430437)",
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+ "at _.requestExecute (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:32:18027)",
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+ "at w.executeCodeCell (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:301076)",
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+ "at w.execute (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:300703)",
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+ "at w.start (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:296367)",
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+ "at runMicrotasks (<anonymous>)",
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+ "at processTicksAndRejections (internal/process/task_queues.js:93:5)",
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+ "at async t.CellExecutionQueue.executeQueuedCells (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:311160)",
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+ "at async t.CellExecutionQueue.start (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:310700)"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2021-10-02T07:00:13.073742Z",
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+ "start_time": "2021-10-02T07:00:12.086679Z"
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+ }
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+ }
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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+ "source": [
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+ "global_random_seed = 3884 # np.random.randint(0, 99999)\n",
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+ "np.random.seed(global_random_seed)"
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+ ],
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+ "outputs": [],
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"metadata": {
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"ExecuteTime": {
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- "end_time": "2021-10-02T03:23:58.323626Z",
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- "start_time": "2021-10-02T03:23:58.316529Z"
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+ "end_time": "2021-10-02T07:00:14.729167Z",
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+ "start_time": "2021-10-02T07:00:14.724918Z"
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}
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- },
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- "outputs": [],
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- "source": [
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- "global_random_seed = 3884 # np.random.randint(0, 99999)\n",
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- "np.random.seed(global_random_seed)"
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- ]
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+ }
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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- "metadata": {
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- "ExecuteTime": {
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- "end_time": "2021-10-02T03:24:01.030972Z",
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- "start_time": "2021-10-02T03:24:01.016971Z"
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- }
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- },
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- "outputs": [],
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"source": [
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"def get_model(input_dim, output_dim, width, depth, regularization_param,\n",
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- " act_func='tanh', output_act_func=False, random_seed=None):\n",
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+ " act_func='tanh', output_act_func=False, random_seed=None):\n",
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" \"\"\"Construct a sequential model based on the input configuration\n",
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"\n",
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" Parameters\n",
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@@ -91,7 +95,7 @@
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" keras.Sequential\n",
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" The constructed model based on the input arguments\n",
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" \"\"\"\n",
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- " initializer = keras.initializers.RandomNormal(seed=random_seed)\n",
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+ " initializer = keras.initializers.RandomNormal()\n",
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" regularizer = keras.regularizers.l2(regularization_param)\n",
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"\n",
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" if act_func == 'sin':\n",
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@@ -99,10 +103,10 @@
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"\n",
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" all_layers = [keras.layers.InputLayer(input_shape=[input_dim])]\n",
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" all_layers += [keras.layers.Dense(width, activation=act_func,\n",
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- " kernel_initializer=initializer,\n",
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- " bias_initializer=initializer,\n",
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- " kernel_regularizer=regularizer,\n",
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- " bias_regularizer=regularizer) for _ in range(depth-1)]\n",
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+ " kernel_initializer=initializer,\n",
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+ " bias_initializer=initializer,\n",
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+ " kernel_regularizer=regularizer,\n",
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+ " bias_regularizer=regularizer) for _ in range(depth-1)]\n",
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" all_layers.append(keras.layers.Dense(\n",
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" output_dim, kernel_initializer=initializer,\n",
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" activation=act_func if output_act_func else None,\n",
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@@ -111,52 +115,169 @@
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" bias_regularizer=regularizer))\n",
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" m = keras.Sequential(all_layers)\n",
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" return m"
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- ]
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+ ],
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+ "outputs": [],
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2021-10-02T07:00:20.353060Z",
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+ "start_time": "2021-10-02T07:00:20.342013Z"
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+ }
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+ }
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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- "metadata": {
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- "ExecuteTime": {
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- "end_time": "2021-10-02T03:24:06.384304Z",
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- "start_time": "2021-10-02T03:24:06.306974Z"
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- }
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- },
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+ "source": [
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+ "tf.keras.backend.clear_session()\n",
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+ "model = get_model(1, 1, 20, 5, 1e-7, act_func='sin', output_act_func=False)"
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+ ],
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"outputs": [
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{
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- "name": "stdout",
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"output_type": "stream",
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+ "name": "stdout",
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"text": [
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- "Metal device set to: Apple M1\n"
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+ "Metal device set to: Apple M1\n",
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+ "\n",
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+ "systemMemory: 16.00 GB\n",
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+ "maxCacheSize: 5.33 GB\n",
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+ "\n"
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]
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},
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{
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+ "output_type": "stream",
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"name": "stderr",
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+ "text": [
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+ "2021-10-02 21:13:31.476496: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
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+ "2021-10-02 21:13:31.476623: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2021-10-02T08:06:56.053459Z",
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+ "start_time": "2021-10-02T08:06:56.002144Z"
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+ }
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+ }
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "source": [
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+ "n_train = 100\n",
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+ "x_train = tf.reshape(tf.linspace(0.0, 1.0, n_train), [100, 1])\n",
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+ "optimizer = keras.optimizers.Adam(learning_rate=1e-2)\n",
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+ "Pe = 10\n",
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+ "Da = 10\n",
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+ "lambda_b = 1000\n",
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+ "with tf.device('/cpu:0'):\n",
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+ " for ep in range(3001):\n",
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+ " with tf.GradientTape() as tape_0:\n",
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+ " with tf.GradientTape() as tape_1:\n",
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+ " tape_1.watch(x_train)\n",
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+ " with tf.GradientTape() as tape_2:\n",
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+ " tape_2.watch(x_train)\n",
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+ " u = model(x_train)\n",
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+ " du_dx = tape_2.gradient(u, x_train)\n",
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+ " d2u_dx2 = tape_1.gradient(du_dx, x_train)\n",
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+ " int_loss = sum(map(lambda d2, d, u: (d2-Pe*d+Da*u*(1-u))**2,\n",
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+ " d2u_dx2, du_dx, u))/n_train\n",
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+ " boundary_loss = lambda_b*(u[0]**2+(u[-1]-1)**2)\n",
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+ " loss = int_loss + boundary_loss + sum(model.losses)\n",
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+ " grads = tape_0.gradient(loss, model.trainable_variables)\n",
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+ " optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
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+ " if not ep % 100 or ep == 1000:\n",
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+ " print(ep, loss)\n"
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+ ],
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+ "outputs": [
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+ {
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"output_type": "stream",
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+ "name": "stdout",
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"text": [
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- "2021-10-01 20:24:06.336225: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
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- "2021-10-01 20:24:06.336513: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
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+ "0 tf.Tensor([1039.3241], shape=(1,), dtype=float32)\n",
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+ "100 tf.Tensor([58.22947], shape=(1,), dtype=float32)\n",
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+ "200 tf.Tensor([56.328785], shape=(1,), dtype=float32)\n",
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+ "300 tf.Tensor([55.604458], shape=(1,), dtype=float32)\n",
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+ "400 tf.Tensor([57.53366], shape=(1,), dtype=float32)\n",
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+ "500 tf.Tensor([49.33706], shape=(1,), dtype=float32)\n",
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+ "600 tf.Tensor([61.896923], shape=(1,), dtype=float32)\n",
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+ "700 tf.Tensor([48.654636], shape=(1,), dtype=float32)\n",
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+ "800 tf.Tensor([79.09517], shape=(1,), dtype=float32)\n",
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+ "900 tf.Tensor([82.4559], shape=(1,), dtype=float32)\n",
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+ "1000 tf.Tensor([34.646717], shape=(1,), dtype=float32)\n",
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+ "1100 tf.Tensor([7.691236], shape=(1,), dtype=float32)\n",
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+ "1200 tf.Tensor([2.3371177], shape=(1,), dtype=float32)\n",
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+ "1300 tf.Tensor([6.0998716], shape=(1,), dtype=float32)\n",
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+ "1400 tf.Tensor([0.35021833], shape=(1,), dtype=float32)\n",
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+ "1500 tf.Tensor([1.1403896], shape=(1,), dtype=float32)\n",
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+ "1600 tf.Tensor([0.11293], shape=(1,), dtype=float32)\n",
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+ "1700 tf.Tensor([2.166767], shape=(1,), dtype=float32)\n",
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+ "1800 tf.Tensor([0.0864344], shape=(1,), dtype=float32)\n",
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+ "1900 tf.Tensor([2.6307728], shape=(1,), dtype=float32)\n",
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+ "2000 tf.Tensor([0.977543], shape=(1,), dtype=float32)\n",
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+ "2100 tf.Tensor([0.2197722], shape=(1,), dtype=float32)\n",
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+ "2200 tf.Tensor([3.6889656], shape=(1,), dtype=float32)\n",
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+ "2300 tf.Tensor([0.08489634], shape=(1,), dtype=float32)\n",
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+ "2400 tf.Tensor([0.5404036], shape=(1,), dtype=float32)\n",
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+ "2500 tf.Tensor([0.10080672], shape=(1,), dtype=float32)\n",
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+ "2600 tf.Tensor([0.17384219], shape=(1,), dtype=float32)\n",
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+ "2700 tf.Tensor([1.1919072], shape=(1,), dtype=float32)\n",
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+ "2800 tf.Tensor([0.7122941], shape=(1,), dtype=float32)\n",
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+ "2900 tf.Tensor([1.3751904], shape=(1,), dtype=float32)\n",
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+ "3000 tf.Tensor([0.14365542], shape=(1,), dtype=float32)\n"
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]
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}
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],
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+ "metadata": {
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+ "ExecuteTime": {
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+ "start_time": "2021-10-02T08:07:24.274Z"
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+ }
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+ }
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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"source": [
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- "tf.keras.backend.clear_session()\n",
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- "model = get_model(1,1,20,5,1.0e-7,act_func='sin',output_act_func=False)"
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- ]
|
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+ "res = np.load(f'ref/diffusion-advection-reaction_pe_{Pe}_da_{Da}.npy')\n",
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+ "plt.figure(figsize=[10,6])\n",
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+ "plt.plot(res[0],res[1],'r')\n",
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+ "pred = model.predict(x_train)\n",
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+ "plt.plot(x_train, pred, 'b--',linewidth=2)"
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+ ],
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+ "outputs": [
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+ {
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+ "output_type": "error",
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+ "ename": "Error",
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+ "evalue": "Kernel is dead",
|
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+ "traceback": [
|
|
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+ "Error: Kernel is dead",
|
|
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+ "at f._sendKernelShellControl (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/node_modules/@jupyterlab/services.js:3:428116)",
|
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+ "at f.sendShellMessage (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/node_modules/@jupyterlab/services.js:3:427885)",
|
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+ "at f.requestExecute (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/node_modules/@jupyterlab/services.js:3:430437)",
|
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+ "at _.requestExecute (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:32:18027)",
|
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+ "at w.executeCodeCell (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:301076)",
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+ "at w.execute (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:300703)",
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+ "at w.start (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:296367)",
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+ "at runMicrotasks (<anonymous>)",
|
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+ "at processTicksAndRejections (internal/process/task_queues.js:93:5)",
|
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+ "at async t.CellExecutionQueue.executeQueuedCells (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:311160)",
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+ "at async t.CellExecutionQueue.start (/Users/zhilong/.vscode/extensions/ms-toolsai.jupyter-2021.8.2041215044/out/client/extension.js:52:310700)"
|
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+ ]
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+ }
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+ ],
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+ "metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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- "metadata": {},
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+ "source": [],
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"outputs": [],
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- "source": []
|
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+ "metadata": {}
|
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}
|
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],
|
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"metadata": {
|
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"kernelspec": {
|
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- "display_name": "Python 3 (ipykernel)",
|
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- "language": "python",
|
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|
- "name": "python3"
|
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+ "name": "python3",
|
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|
+ "display_name": "Python 3.8.12 64-bit ('tf': conda)"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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|
@@ -198,8 +319,11 @@
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"_Feature"
|
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],
|
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|
"window_display": false
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+ },
|
|
|
+ "interpreter": {
|
|
|
+ "hash": "859cadc33cf9c93d9354f622d0bfdc75f7d75fc23a9b22a18dfc646072acf83e"
|
|
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}
|
|
|
},
|
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"nbformat": 4,
|
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|
"nbformat_minor": 2
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-}
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+}
|
