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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bD0MZfilv6Cc"
},
"outputs": [],
"source": [
"import numpy as np\n",
"def task_seperate(x,y):\n",
"\n",
" indx0 = np.where(y==0)[0]\n",
" y0 = y[indx0]\n",
" x0 = x[indx0,:,:,:]\n",
"\n",
" indx1 = np.where(y==1)[0]\n",
" y1 = y[indx1]\n",
" x1 = x[indx1,:,:,:]\n",
"\n",
" indx2 = np.where(y==2)[0]\n",
" y2 = y[indx2]\n",
" x2 = x[indx2,:,:,:]\n",
"\n",
" indx3 = np.where(y==3)[0]\n",
" y3 = y[indx3]\n",
" x3 = x[indx3,:,:,:]\n",
"\n",
" indx4 = np.where(y==4)[0]\n",
" y4 = y[indx4]\n",
" x4 = x[indx4,:,:,:]\n",
"\n",
" indx5 = np.where(y==5)[0]\n",
" y5 = y[indx5]\n",
" x5 = x[indx5,:,:,:]\n",
"\n",
" y_task1 = np.concatenate((y0,y1),axis=0)\n",
" x_task1 = np.concatenate((x0,x1),axis=0)\n",
"\n",
" y_task2 = np.concatenate((y2,y3),axis=0)\n",
" x_task2 = np.concatenate((x2,x3),axis=0)\n",
"\n",
" y_task3 = np.concatenate((y4,y5),axis=0)\n",
" x_task3 = np.concatenate((x4,x5),axis=0)\n",
"\n",
" Y = [y_task1, y_task2, y_task3]\n",
" X = [x_task1, x_task2, x_task3]\n",
"\n",
" return X,Y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Q2qu1tupEhIf"
},
"outputs": [],
"source": [
"def compile_model(model, learning_rate, extra_losses=None):\n",
" def custom_loss(y_true, y_pred):\n",
" loss = keras.losses.sparse_categorical_crossentropy(y_true, y_pred)\n",
" if extra_losses is not None:\n",
" for fn in extra_losses:\n",
" loss += fn(model)\n",
"\n",
" return loss\n",
"\n",
" model.compile(\n",
" loss=custom_loss,\n",
" optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
" metrics=[\"accuracy\"]\n",
" )\n",
"\n",
"def report(model, epoch, validation_datasets, batch_size):\n",
" result = []\n",
" for inputs, labels in validation_datasets:\n",
" _, accuracy = model.evaluate(inputs, labels, verbose=0,\n",
" batch_size=batch_size)\n",
" result.append(\"{:.2f}\".format(accuracy * 100))\n",
"\n",
" # Add 1: assuming that we report after training has finished for this epoch.\n",
" print(epoch + 1, \"\\t\", \"\\t\".join(result))\n",
"\n",
"def train_epoch(model, train_data, batch_size,\n",
" gradient_mask=None, incdet_threshold=None):\n",
" \"\"\"Need a custom training loop for when we modify the gradients.\"\"\"\n",
" dataset = tf.data.Dataset.from_tensor_slices(train_data)\n",
" dataset = dataset.shuffle(len(train_data[0])).batch(batch_size)\n",
"\n",
" for inputs, labels in dataset:\n",
" with tf.GradientTape() as tape:\n",
" outputs = model(inputs)\n",
" loss = model.compiled_loss(labels, outputs)\n",
"\n",
" gradients = tape.gradient(loss, model.trainable_weights)\n",
"\n",
" model.optimizer.apply_gradients(zip(gradients, model.trainable_weights))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3GlD4yJOvr9n"
},
"outputs": [],
"source": [
"def fisher_matrix(model, dataset, samples):\n",
" \"\"\"\n",
" Compute the Fisher matrix, representing the importance of each weight in the\n",
" model. This is approximated using the variance of the gradient of each\n",
" weight, for some number of samples from the dataset.\n",
"\n",
" :param model: Model whose Fisher matrix is to be computed.\n",
" :param dataset: Dataset which the model has been trained on, but which will\n",
" not be seen in the future. Formatted as (inputs, labels).\n",
" :param samples: Number of samples to take from the dataset. More samples\n",
" gives a better approximation of the true variance.\n",
" :return: The main diagonal of the Fisher matrix, shaped to match the weights\n",
" returned by `model.trainable_weights`.\n",
" \"\"\"\n",
" inputs, labels = dataset\n",
" weights = model.trainable_weights\n",
" variance = [tf.zeros_like(tensor) for tensor in weights]\n",
"\n",
" for _ in range(samples):\n",
" # Select a random element from the dataset.\n",
" index = np.random.randint(len(inputs))\n",
" data = inputs[index]\n",
"\n",
" # When extracting from the array we lost a dimension so put it back.\n",
" data = tf.expand_dims(data, axis=0)\n",
"\n",
" # Collect gradients.\n",
" with tf.GradientTape() as tape:\n",
" output = model(data)\n",
" log_likelihood = tf.math.log(output)\n",
"\n",
" gradients = tape.gradient(log_likelihood, weights)\n",
"\n",
" # If the model has converged, we can assume that the current weights\n",
" # are the mean, and each gradient we see is a deviation. The variance is\n",
" # the average of the square of this deviation.\n",
" variance = [var + (grad ** 2) for var, grad in zip(variance, gradients)]\n",
"\n",
" fisher_diagonal = [tensor / samples for tensor in variance]\n",
" return fisher_diagonal\n",
"\n",
"\n",
"def ewc_loss(lam, model, dataset, samples):\n",
" \"\"\"\n",
" Generate a loss function which will penalise divergence from the current\n",
" state. It is assumed that the model achieves good accuracy on `dataset`,\n",
" and we want to preserve this behaviour.\n",
"\n",
" The penalty is scaled according to how important each weight is for the\n",
" given dataset, and `lam` (lambda) applies equally to all weights.\n",
"\n",
" :param lam: Weight of this cost function compared to the other losses.\n",
" :param model: Model optimised for the given dataset.\n",
" :param dataset: NumPy arrays (inputs, labels).\n",
" :param samples: Number of samples of dataset to take when estimating\n",
" importance of weights. More samples improves estimates.\n",
" :return: A loss function.\n",
" \"\"\"\n",
" optimal_weights = deepcopy(model.trainable_weights)\n",
" fisher_diagonal = fisher_matrix(model, dataset, samples)\n",
"\n",
" def loss_fn(new_model):\n",
" # We're computing:\n",
" # sum [(lambda / 2) * F * (current weights - optimal weights)^2]\n",
" loss = 0\n",
" current = new_model.trainable_weights\n",
" for f, c, o in zip(fisher_diagonal, current, optimal_weights):\n",
" loss += tf.reduce_sum(f * ((c - o) ** 2))\n",
"\n",
" return loss * (lam / 2)\n",
"\n",
" return loss_fn\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "K8M29Gfrtwwe",
"outputId": "97a40ad6-b9ed-432d-c889-e313b0c64bb1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11490434/11490434 [==============================] - 0s 0us/step\n",
"Model Trained on Task 0\n",
"67/67 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9995\n",
"Test Accuracy on Task 0 = 1.00\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:5 out of the last 1585 calls to <function _BaseOptimizer._update_step_xla at 0x7e7317df1090> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Trained on Task 1\n",
"67/67 [==============================] - 1s 2ms/step - loss: 4.1902 - accuracy: 0.1225\n",
"Test Accuracy on Task 0 = 0.12\n",
"64/64 [==============================] - 0s 3ms/step - loss: 0.0728 - accuracy: 0.9799\n",
"Test Accuracy on Task 1 = 0.98\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:5 out of the last 1513 calls to <function _BaseOptimizer._update_step_xla at 0x7e7306532320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Trained on Task 2\n",
"67/67 [==============================] - 0s 3ms/step - loss: 7.8403 - accuracy: 0.0473\n",
"Test Accuracy on Task 0 = 0.05\n",
"64/64 [==============================] - 0s 2ms/step - loss: 6.0971 - accuracy: 0.0034\n",
"Test Accuracy on Task 1 = 0.00\n",
"59/59 [==============================] - 0s 2ms/step - loss: 0.3602 - accuracy: 0.9536\n",
"Test Accuracy on Task 2 = 0.95\n"
]
}
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from keras.datasets import mnist\n",
"from copy import deepcopy\n",
"\n",
"# Hyperparameters\n",
"learning_rate = 0.001\n",
"epochs = 2\n",
"lambda_ewc = 10 # Importance of past tasks\n",
"\n",
"# Load MNIST dataset\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"x_train = x_train.reshape(-1, 28, 28, 1).astype(\"float32\") / 255.0\n",
"x_test = x_test.reshape(-1, 28, 28, 1).astype(\"float32\") / 255.0\n",
"\n",
"x_train_task, y_train_task = task_seperate(x_train,y_train)\n",
"x_test_task, y_test_task = task_seperate(x_test,y_test)\n",
"\n",
"# Define model (replace with your desired architecture)\n",
"model = keras.Sequential([\n",
" keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
" keras.layers.Dense(128, activation=\"relu\"),\n",
" keras.layers.Dense(10, activation=\"softmax\")\n",
"])\n",
"\n",
"# Compile model with Adam optimizer\n",
"compile_model(model, learning_rate)\n",
"\n",
"regularisers = []\n",
"\n",
"for task in range(3):\n",
" inputs = x_train_task[task]\n",
" labels = y_train_task[task]\n",
"\n",
" for epoch in range(epochs):\n",
" train_epoch(model, (inputs, labels), batch_size=64)\n",
" valid_sets = [(x_test_task[task], y_test_task[task])]\n",
"\n",
" print(f\"Model Trained on Task {task}\")\n",
"\n",
"\n",
" for iTask in range(task+1):\n",
" test_loss, test_acc = model.evaluate(x_test_task[iTask], y_test_task[iTask])\n",
" print(f\"Test Accuracy on Task {iTask} = {test_acc:.2f}\")\n",
"\n",
" loss_fn = ewc_loss(lambda_ewc, model, (inputs, labels), x_train_task[task].shape[0])\n",
" regularisers.append(loss_fn)\n",
" compile_model(model, learning_rate, extra_losses=regularisers)\n",
"\n"
]
}
],
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"nbformat": 4,
"nbformat_minor": 0
} |