{ "cells": [ { "cell_type": "markdown", "id": "d1a7f0ec", "metadata": {}, "source": [ "# Train RNN to Solve Parametric Working Memory\n", "\n", "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/recurrent_networks/ParametricWorkingMemory.ipynb)\n", "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/recurrent_networks/ParametricWorkingMemory.ipynb)" ] }, { "cell_type": "code", "execution_count": 23, "id": "4568569b", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:15.957230Z", "start_time": "2023-07-22T12:19:15.927464600Z" } }, "outputs": [], "source": [ "import brainpy as bp\n", "import brainpy.math as bm\n", "import brainpy_datasets as bd\n", "\n", "bp.math.set_platform('cpu')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:34.111397800Z", "start_time": "2023-07-22T12:19:34.097435300Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('2.4.3', '0.0.0.6')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp.__version__, bd.__version__" ] }, { "cell_type": "code", "execution_count": 25, "id": "6e6338ac", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:15.989007700Z", "start_time": "2023-07-22T12:19:15.957230Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:15.992513500Z", "start_time": "2023-07-22T12:19:15.974069700Z" }, "collapsed": false }, "outputs": [], "source": [ "dataset = bd.cognitive.RateDelayComparison(t_decision=500.)\n", "task = bd.cognitive.TaskLoader(dataset, batch_size=16)" ] }, { "cell_type": "code", "execution_count": 27, "id": "4866ba2a", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:16.020656200Z", "start_time": "2023-07-22T12:19:15.997039900Z" } }, "outputs": [], "source": [ "class RNN(bp.DynamicalSystem):\n", " def __init__(self, num_input, num_hidden, num_output, num_batch,\n", " w_ir=bp.init.KaimingNormal(scale=1.),\n", " w_rr=bp.init.KaimingNormal(scale=1.),\n", " w_ro=bp.init.KaimingNormal(scale=1.),\n", " dt=None, seed=None):\n", " super(RNN, self).__init__()\n", "\n", " # parameters\n", " self.tau = 100\n", " self.num_batch = num_batch\n", " self.num_input = num_input\n", " self.num_hidden = num_hidden\n", " self.num_output = num_output\n", " if dt is None:\n", " self.alpha = 1\n", " else:\n", " self.alpha = dt / self.tau\n", " self.rng = bm.random.RandomState(seed=seed)\n", "\n", " # input weight\n", " self.w_ir = bm.TrainVar(bp.init.parameter(w_ir, (num_input, num_hidden)))\n", "\n", " # recurrent weight\n", " bound = 1 / num_hidden ** 0.5\n", " self.w_rr = bm.TrainVar(bp.init.parameter(w_rr, (num_hidden, num_hidden)))\n", " self.b_rr = bm.TrainVar(self.rng.uniform(-bound, bound, num_hidden))\n", "\n", " # readout weight\n", " self.w_ro = bm.TrainVar(bp.init.parameter(w_ro, (num_hidden, num_output)))\n", " self.b_ro = bm.TrainVar(self.rng.uniform(-bound, bound, num_output))\n", "\n", " self.reset_state(self.mode)\n", "\n", " def reset_state(self, batch_size):\n", " self.h = bp.init.variable_(bm.zeros, self.num_hidden, batch_size)\n", " self.o = bp.init.variable_(bm.zeros, self.num_output, batch_size)\n", "\n", " def cell(self, x, h):\n", " ins = x @ self.w_ir + h @ self.w_rr + self.b_rr\n", " state = h * (1 - self.alpha) + ins * self.alpha\n", " return bm.relu(state)\n", "\n", " def readout(self, h):\n", " return h @ self.w_ro + self.b_ro\n", "\n", " def update(self, x):\n", " self.h.value = self.cell(x, self.h.value)\n", " self.o.value = self.readout(self.h.value)\n", " return self.h.value, self.o.value\n", "\n", " @bm.cls_jit\n", " def predict(self, xs):\n", " self.h[:] = 0.\n", " return bm.for_loop(self.update, xs)\n", "\n", " def loss(self, xs, ys):\n", " hs, os = self.predict(xs)\n", " os = os.reshape((-1, os.shape[-1]))\n", " loss = bp.losses.cross_entropy_loss(os, ys.flatten())\n", " return loss, os" ] }, { "cell_type": "code", "execution_count": 28, "id": "54c6d5d3", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:16.027165400Z", "start_time": "2023-07-22T12:19:16.020656200Z" } }, "outputs": [], "source": [ "# Instantiate the network and print information\n", "hidden_size = 64\n", "with bm.environment(mode=bm.TrainingMode(batch_size=16)):\n", " net = RNN(num_input=dataset.num_inputs,\n", " num_hidden=hidden_size,\n", " num_output=dataset.num_outputs,\n", " num_batch=task.batch_size,\n", " dt=dataset.dt)" ] }, { "cell_type": "code", "execution_count": 29, "id": "be6a825b", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:16.037071400Z", "start_time": "2023-07-22T12:19:16.020656200Z" } }, "outputs": [], "source": [ "predict = bm.jit(net.predict)" ] }, { "cell_type": "code", "execution_count": 30, "id": "9583fb87", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:16.063251700Z", "start_time": "2023-07-22T12:19:16.037071400Z" } }, "outputs": [], "source": [ "# Adam optimizer\n", "opt = bp.optim.Adam(lr=0.001, train_vars=net.train_vars().unique())" ] }, { "cell_type": "code", "execution_count": 31, "id": "b6471f65", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:16.080270600Z", "start_time": "2023-07-22T12:19:16.060751100Z" } }, "outputs": [], "source": [ "# gradient function\n", "grad = bm.grad(net.loss,\n", " grad_vars=net.train_vars().unique(),\n", " return_value=True,\n", " has_aux=True)" ] }, { "cell_type": "code", "execution_count": 32, "id": "c6fad51c", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:16.097455200Z", "start_time": "2023-07-22T12:19:16.080270600Z" } }, "outputs": [], "source": [ "@bm.jit\n", "def train(xs, ys):\n", " grads, loss, os = grad(xs, ys)\n", " opt.update(grads)\n", " return loss, os" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:18.502394300Z", "start_time": "2023-07-22T12:19:16.097455200Z" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch 1, Loss 0.4951, Acc 0.070\n", "Batch 2, Loss 0.1510, Acc 0.696\n", "Batch 3, Loss 0.0696, Acc 0.902\n", "Batch 4, Loss 0.0520, Acc 0.922\n", "Batch 5, Loss 0.0458, Acc 0.920\n", "Batch 6, Loss 0.0433, Acc 0.936\n", "Batch 7, Loss 0.0357, Acc 0.941\n", "Batch 8, Loss 0.0337, Acc 0.943\n", "Batch 9, Loss 0.0274, Acc 0.958\n", "Batch 10, Loss 0.0262, Acc 0.959\n", "Batch 11, Loss 0.0293, Acc 0.950\n", "Batch 12, Loss 0.0248, Acc 0.966\n", "Batch 13, Loss 0.0240, Acc 0.960\n", "Batch 14, Loss 0.0185, Acc 0.971\n", "Batch 15, Loss 0.0154, Acc 0.979\n", "Batch 16, Loss 0.0258, Acc 0.957\n", "Batch 17, Loss 0.0205, Acc 0.967\n", "Batch 18, Loss 0.0224, Acc 0.954\n", "Batch 19, Loss 0.0206, Acc 0.966\n", "Batch 20, Loss 0.0213, Acc 0.966\n" ] } ], "source": [ "running_acc = []\n", "running_loss = []\n", "for i_batch in range(20):\n", " for X, Y in task:\n", " # training\n", " loss, outputs = train(bm.asarray(X), bm.asarray(Y))\n", " # Compute performance\n", " output_np = np.asarray(bm.argmax(outputs, axis=-1)).flatten()\n", " labels_np = np.asarray(Y).flatten()\n", " ind = labels_np > 0 # 0: fixation, 1: choice 1, 2: choice 2\n", " running_loss.append(loss)\n", " running_acc.append(np.mean(labels_np[ind] == output_np[ind]))\n", " print(f'Batch {i_batch + 1}, Loss {np.mean(running_loss):0.4f}, Acc {np.mean(running_acc):0.3f}')\n", " running_loss = []\n", " running_acc = []" ] }, { "cell_type": "code", "execution_count": 34, "id": "f555d95e", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:18.518553600Z", "start_time": "2023-07-22T12:19:18.502394300Z" } }, "outputs": [], "source": [ "def run(num_trial=1):\n", " net.reset_state(num_trial)\n", " inputs, trial_infos = bd.cognitive.TaskLoader(dataset, batch_size=num_trial).get_batch()\n", " rnn_activity, action_pred = predict(inputs)\n", " rnn_activity = bm.as_numpy(rnn_activity)\n", "\n", " # Concatenate activity for PCA\n", " pca = PCA(n_components=2)\n", " activity = rnn_activity.reshape(-1, net.num_hidden)\n", " pca.fit(activity)\n", " # print('Shape of the projected activity: (Time points, PCs): ', activity_pc.shape)\n", "\n", " fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, sharex=True, figsize=(6, 3))\n", " for i in range(num_trial):\n", " activity_pc = pca.transform(rnn_activity[:, i])\n", " color = 'red' if trial_infos[-1, i] == 1 else 'blue'\n", " _ = ax1.plot(activity_pc[:, 0], activity_pc[:, 1], 'o-', color=color)\n", " if i < 3:\n", " _ = ax2.plot(activity_pc[:, 0], activity_pc[:, 1], 'o-', color=color)\n", " ax1.set_xlabel('PC 1')\n", " ax1.set_ylabel('PC 2')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "id": "7fddce83", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:18.662142700Z", "start_time": "2023-07-22T12:19:18.518553600Z" } }, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run(num_trial=20)" ] }, { "cell_type": "code", "execution_count": 37, "id": "8ae8346b", "metadata": { "ExecuteTime": { "end_time": "2023-07-22T12:19:19.062972900Z", "start_time": "2023-07-22T12:19:18.839201500Z" } }, "outputs": [ { "data": { "image/png": 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", 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