{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "9c91ef58", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import tensorflow as tf\n", "import tensorflow.keras.backend as K\n", "from tensorflow.keras import layers\n", "\n", "import collections\n", "import logging\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler, ModelCheckpoint, ReduceLROnPlateau\n", "import pandas as pd\n", "from sklearn.preprocessing import StandardScaler\n", "import os\n", "import math\n", "import gc" ] }, { "cell_type": "code", "execution_count": null, "id": "9a965750", "metadata": {}, "outputs": [], "source": [ "name = \"ETTm1\" # dataset name\n", "seq_len = 512\n", "\n", "batch_size = 32\n", "pred_len = 192\n", "\n", "feature_type = \"M\"\n", "target = \"\"\n", "learning_rate = 0.0001\n", "patience = 5\n", "\n", "num_heads=1\n", "d_model=16\n", "rho = 0.1" ] }, { "cell_type": "code", "execution_count": null, "id": "d17cf421", "metadata": {}, "outputs": [], "source": [ "# please provide your own absolute links here\n", "LOCAL_CACHE_DIR = '../Data/Benchmark/' # please include your dataset directory here\n", "\n", "checkpoint_path = \"../Train_SAM/models/checkpoint\" + name + str(pred_len) + \"_SAM\" + \".model\" # where the model checkpoint should be saved + the .model file type\n", "name_df = \"../Results_SAM/\" + name + \"_\" + str(pred_len) + \"_SAM\" + \".csv\" # a filepath .csv file type to save the mse and mae results" ] }, { "cell_type": "code", "execution_count": null, "id": "5a6e0049", "metadata": {}, "outputs": [], "source": [ "class CaptureWeightsCallback(tf.keras.callbacks.Callback):\n", " \"\"\"\n", " Custom TensorFlow callback for capturing and logging model weights during training, with a focus on attention weights.\n", "\n", " This callback is designed to monitor the evolution of model weights, particularly attention weights, across training epochs.\n", " It facilitates the analysis of training dynamics and model behavior by storing weight snapshots at specified intervals.\n", "\n", " Attributes:\n", " model (tf.keras.Model): Instance of the TensorFlow model being trained. The model should have a method\n", " `get_last_attention_weights()` that this callback can invoke to obtain attention weights.\n", " attention_weights_history (list): Accumulates the attention weights captured at the end of specified epochs. \n", " This history facilitates post-training analysis of weight adjustments.\n", "\n", " Methods:\n", " on_epoch_end(epoch, logs=None): Overrides the base class method to capture attention weights at the end of each epoch.\n", " Weights are captured based on specified criteria, e.g., every 5 epochs.\n", " get_attention_weights_history(): Provides access to the accumulated history of attention weights captured during training.\n", " \"\"\"\n", " \n", " def __init__(self, model):\n", " \"\"\"\n", " Initializes the callback with a specific model to monitor its attention weights during training.\n", "\n", " Parameters:\n", " model (tf.keras.Model): The model whose attention weights are to be monitored and captured.\n", " \"\"\"\n", " super().__init__()\n", " self.model = model\n", " self.penultimate_weights = None\n", " self.attention_weights_history = []\n", "\n", " def on_epoch_end(self, epoch, logs=None):\n", " \"\"\"\n", " Called at the end of an epoch during training to capture and store attention weights if the current\n", " epoch satisfies the capture criteria (e.g., every 5 epochs).\n", "\n", " Parameters:\n", " epoch (int): The current epoch number.\n", " logs (dict): Currently unused. Contains logs from the training epoch.\n", " \"\"\"\n", " if epoch % 5 == 0: # Perform analysis every 5 epochs\n", " # Retrieve attention weights from the model\n", " last_attention_weights = self.model.get_last_attention_weights()\n", " if last_attention_weights is not None:\n", " self.attention_weights_history.append(last_attention_weights)\n", " \n", " def get_attention_weights_history(self):\n", " \"\"\"\n", " Returns the history of attention weights captured during training.\n", "\n", " Returns:\n", " A list of attention weights captured at specified intervals during training.\n", " \"\"\"\n", " return self.attention_weights_history" ] }, { "cell_type": "code", "execution_count": null, "id": "22e2e8be", "metadata": {}, "outputs": [], "source": [ "def setup_callbacks(learning_rate, patience, checkpoint_path, model):\n", " \"\"\"\n", " Sets up and returns TensorFlow callbacks for use during model training. These callbacks include early stopping,\n", " learning rate scheduling, model checkpointing, and a custom callback for capturing model weights.\n", "\n", " This function is tailored to support flexible training configurations, allowing for dynamic adjustment of training\n", " behavior based on model performance and training progress.\n", "\n", " Parameters:\n", " args (argparse.Namespace): Parsed command-line arguments containing training configurations such as patience for early stopping,\n", " total training epochs, and initial learning rate.\n", " checkpoint_path (str): File path where model checkpoints will be saved. The best model according to validation loss is checkpointed.\n", " model (tf.keras.Model): The TensorFlow model being trained. Required for initializing the `CaptureWeightsCallback`.\n", " model_name (str): Name of the model being trained. This can be used to adjust callback behavior for different models.\n", "\n", " Returns:\n", " tuple: A tuple containing:\n", " - A list of TensorFlow callbacks configured for the training session.\n", " - An instance of `CaptureWeightsCallback`, which can be used post-training to access captured weights.\n", "\n", " Raises:\n", " Exception: If an error occurs in the setup of callbacks, an exception is logged and raised to prevent silent training failures.\n", "\n", " Example:\n", " >>> callbacks, capture_weights_callback = setup_callbacks(args, './model_checkpoints', model, 'my_model')\n", " This example demonstrates how to invoke `setup_callbacks` to obtain configured callbacks for training, including a custom\n", " weight capture callback for post-training analysis.\n", " \"\"\"\n", " try:\n", " checkpoint_callback = ModelCheckpoint(\n", " filepath=checkpoint_path,\n", " monitor='val_loss', \n", " verbose=1,\n", " save_best_only=True,\n", " save_weights_only=True,\n", " )\n", "\n", " early_stop_callback = EarlyStopping(\n", " monitor='val_loss',\n", " patience=patience,\n", " verbose=1,\n", " )\n", "\n", " lr_schedule_callback = LearningRateScheduler(\n", " lambda epoch: cosine_annealing(epoch, 5, learning_rate, 1e-6),\n", " verbose=1,\n", " )\n", "\n", " lrdecay = ReduceLROnPlateau(monitor='val_loss', factor=0.90, patience=3, min_lr=learning_rate * 0.001, verbose=1)\n", "\n", " capture_weights_callback = CaptureWeightsCallback(model)\n", "\n", " callbacks = [checkpoint_callback, lr_schedule_callback, capture_weights_callback, early_stop_callback]\n", " \n", " return callbacks, capture_weights_callback\n", " except Exception as e:\n", " logging.error(f\"Error setting up callbacks: {e}\")\n", " raise\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5604ad15", "metadata": {}, "outputs": [], "source": [ "def cosine_annealing(epoch, max_epochs, initial_lr, min_lr):\n", " \"\"\"\n", " Applies cosine annealing to the learning rate.\n", " \n", " Parameters:\n", " epoch (int): Current epoch.\n", " max_epochs (int): Maximum number of epochs.\n", " initial_lr (float): Initial learning rate.\n", " min_lr (float): Minimum learning rate.\n", " \n", " Returns:\n", " float: Adjusted learning rate.\n", " \"\"\"\n", " cos_inner = (math.pi * (epoch % max_epochs)) / max_epochs\n", " return min_lr + (initial_lr - min_lr) * (math.cos(cos_inner) + 1) / 2\n" ] }, { "cell_type": "code", "execution_count": null, "id": "430e2b9e", "metadata": {}, "outputs": [], "source": [ "class RevNorm(layers.Layer):\n", " \"\"\"\n", " Implements Reversible Instance Normalization (RevNorm).\n", "\n", " This layer normalizes input features per instance and can reverse the normalization process. It is designed\n", " to maintain the statistical properties of the input data, making it particularly useful in generative models\n", " where the exact inverse operation is necessary.\n", "\n", " Attributes:\n", " axis (int): The axis along which to compute the mean and standard deviation for normalization.\n", " eps (float): A small constant added to the standard deviation to prevent division by zero.\n", " affine (bool): Whether to apply a learnable affine transformation after normalization.\n", "\n", " The original implementation can be found in the Google Research repository:\n", " https://github.com/google-research/google-research/blob/master/tsmixer/tsmixer_basic/models/rev_in.py\n", "\n", " Example usage:\n", " rev_norm = RevNorm(axis=-1, eps=1e-5, affine=True)\n", " normalized_output = rev_norm(input_tensor, mode='norm')\n", " denormalized_output = rev_norm(normalized_output, mode='denorm', target_slice=slice_indices)\n", " \"\"\"\n", "\n", " def __init__(self, axis, eps=1e-5, affine=True):\n", " super().__init__()\n", " self.axis = axis # Defines the dimension along which normalization is performed.\n", " self.eps = eps # Small epsilon value to ensure numerical stability.\n", " self.affine = affine # Determines if learnable affine parameters should be used.\n", "\n", " def build(self, input_shape):\n", " \"\"\"\n", " Initializes the layer's weights.\n", "\n", " This method creates affine transformation weights if the `affine` attribute is set to True.\n", " It defines two trainable weights, `affine_weight` and `affine_bias`, which are used to scale and shift\n", " the normalized data respectively.\n", "\n", " Args:\n", " input_shape (TensorShape): The shape of the input tensor to the layer. The last dimension is used\n", " to determine the shape of the affine weights.\n", "\n", " Note: This method is automatically called during the first use of the layer.\n", " \"\"\"\n", " if self.affine:\n", " self.affine_weight = self.add_weight(\n", " 'affine_weight', shape=input_shape[-1], initializer='ones'\n", " )\n", " self.affine_bias = self.add_weight(\n", " 'affine_bias', shape=input_shape[-1], initializer='zeros'\n", " )\n", "\n", " def call(self, x, mode, target_slice=None):\n", " \"\"\"\n", " Performs normalization or denormalization on the input tensor.\n", "\n", " Args:\n", " x (Tensor): Input tensor to be normalized or denormalized.\n", " mode (str): 'norm' for normalization and 'denorm' for denormalization.\n", " target_slice (slice, optional): Target slice for denormalization.\n", "\n", " Returns:\n", " Tensor: The normalized or denormalized output.\n", " \"\"\"\n", " if mode == 'norm':\n", " self._get_statistics(x)\n", " x = self._normalize(x)\n", " elif mode == 'denorm':\n", " x = self._denormalize(x, target_slice)\n", " else:\n", " raise NotImplementedError\n", " return x\n", "\n", " def _get_statistics(self, x):\n", " \"\"\"\n", " Computes the mean and standard deviation of the input tensor along the specified axis.\n", "\n", " The calculated mean and standard deviation are used for normalizing the input data. They are computed\n", " using `tf.reduce_mean` and `tf.sqrt(tf.reduce_variance(...) + self.eps)` to ensure numerical stability.\n", "\n", " Args:\n", " x (Tensor): Input tensor from which the statistics are computed.\n", "\n", " Updates:\n", " self.mean (Tensor): The mean of the input tensor, calculated along the specified axis.\n", " self.stdev (Tensor): The standard deviation of the input tensor, ensuring numerical stability by adding `self.eps`.\n", " \"\"\"\n", " self.mean = tf.stop_gradient(\n", " tf.reduce_mean(x, axis=self.axis, keepdims=True)\n", " )\n", " self.stdev = tf.stop_gradient(\n", " tf.sqrt(\n", " tf.math.reduce_variance(x, axis=self.axis, keepdims=True) + self.eps\n", " )\n", " )\n", "\n", " def _normalize(self, x):\n", " \"\"\"\n", " Normalizes the input tensor using the computed mean and standard deviation.\n", "\n", " This method subtracts the mean from the input tensor and divides it by the standard deviation, effectively\n", " standardizing the input to have a mean of 0 and a standard deviation of 1. If affine transformation is enabled,\n", " it further applies scaling and shifting to the standardized input.\n", "\n", " Args:\n", " x (Tensor): Input tensor to be normalized.\n", "\n", " Returns:\n", " Tensor: The normalized tensor.\n", " \"\"\"\n", " x = x - self.mean\n", " x = x / self.stdev\n", " if self.affine:\n", " x = x * self.affine_weight\n", " x = x + self.affine_bias\n", " return x\n", "\n", " def _denormalize(self, x, target_slice=None):\n", " \"\"\"\n", " Reverses the normalization process for the given slice of the input tensor.\n", "\n", " This method applies the inverse of the normalization operation. If affine transformation was applied during\n", " normalization, it reverses this process first. Then, it multiplies the tensor by the standard deviation and adds\n", " the mean to denormalize the data.\n", "\n", " Args:\n", " x (Tensor): Normalized tensor that needs to be denormalized.\n", " target_slice (slice, optional): Specific slice of the tensor to denormalize. Useful when different parts\n", " of the tensor require different reverse operations.\n", "\n", " Returns:\n", " Tensor: The denormalized tensor.\n", " \"\"\"\n", " if self.affine:\n", " x = x - self.affine_bias[target_slice]\n", " x = x / self.affine_weight[target_slice]\n", " x = x * self.stdev[:, :, target_slice]\n", " x = x + self.mean[:, :, target_slice]\n", " return x\n" ] }, { "cell_type": "code", "execution_count": null, "id": "323930c3", "metadata": {}, "outputs": [], "source": [ "class SAM:\n", " \"\"\"\n", " Sharpness-Aware Minimization (SAM) for Enhanced Training Stability.\n", " \n", " SAM optimizes a model's parameters in the direction that enhances model\n", " performance while simultaneously minimizing loss sharpness, aiming to improve\n", " generalization. This implementation wraps around a base TensorFlow optimizer\n", " to apply the SAM methodology.\n", " \n", " Reference:\n", " \"Sharpness-Aware Minimization for Efficiently Improving Generalization\"\n", " by Foret, Kleiner, Mobahi, and Neyshabur. https://openreview.net/pdf?id=6Tm1mposlrM\n", "\n", " The original implementation can be found at:\n", " - https://github.com/davda54/sam\n", " \n", " Attributes:\n", " base_optimizer (tf.keras.optimizers.Optimizer): The TensorFlow optimizer to wrap.\n", " rho (float): The neighborhood size for sharpness-aware optimization.\n", " eps (float): A small epsilon value to prevent division by zero.\n", " \"\"\"\n", "\n", " def __init__(self, base_optimizer, rho=0.05, eps=1e-12):\n", " \"\"\"\n", " Initializes the SAM optimizer wrapper.\n", " \n", " Parameters:\n", " base_optimizer (tf.keras.optimizers.Optimizer): The base optimizer.\n", " rho (float): The neighborhood size for sharpness-aware optimization.\n", " eps (float): A small epsilon value to prevent division by zero.\n", " \"\"\"\n", " assert rho >= 0.0, f\"Invalid rho, should be non-negative: {rho}\"\n", " self.rho = rho\n", " self.eps = eps\n", " self.base_optimizer = base_optimizer\n", "\n", " def first_step(self, gradients, trainable_vars):\n", " \"\"\"\n", " Performs the first optimization step, moving weights in the direction\n", " that increases loss sharpness.\n", " \n", " Parameters:\n", " gradients (List[tf.Tensor]): Gradients of the loss with respect to the model parameters.\n", " trainable_vars (List[tf.Variable]): The model's trainable variables.\n", " \"\"\"\n", " self.e_ws = []\n", " grad_norm = tf.linalg.global_norm(gradients)\n", " ew_multiplier = self.rho / (grad_norm + self.eps)\n", "\n", " for i in range(len(trainable_vars)):\n", " e_w = tf.math.multiply(gradients[i], ew_multiplier)\n", " trainable_vars[i].assign_add(e_w)\n", " self.e_ws.append(e_w)\n", "\n", " def second_step(self, gradients, trainable_variables):\n", " \"\"\"\n", " Performs the second optimization step, applying the base optimizer\n", " update after reverting the first step's perturbation.\n", " \n", " Parameters:\n", " gradients (List[tf.Tensor]): Gradients of the loss with respect to the model parameters after the first step.\n", " trainable_variables (List[tf.Variable]): The model's trainable variables.\n", " \"\"\"\n", " for i in range(len(trainable_variables)):\n", " trainable_variables[i].assign_add(-self.e_ws[i]) # Revert first step\n", " self.base_optimizer.apply_gradients(zip(gradients, trainable_variables))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3adf7700", "metadata": {}, "outputs": [], "source": [ "class SpectralNormalizedAttention(layers.MultiHeadAttention):\n", " \"\"\"\n", " Spectral Normalized Multi-Head Attention Layer.\n", " \n", " This layer extends the MultiHeadAttention layer with spectral normalization on the\n", " query, key, and value weights, implementing the sigma-reparam method described in\n", " \"Stabilizing Transformer Training by Preventing Attention Entropy Collapse\".\n", " \n", " Paper URL: https://openreview.net/forum?id=LL8gz8FHxH\n", " GitHub Code: https://github.com/apple/ml-sigma-reparam\n", " \n", " Attributes:\n", " gamma (tf.Variable): Scaling factor for the normalized weights, trainable.\n", " \"\"\"\n", "\n", " def __init__(self, *args, **kwargs):\n", " \"\"\"Initializes the SpectralNormalizedAttention layer with standard arguments for MultiHeadAttention.\"\"\"\n", " super(SpectralNormalizedAttention, self).__init__(*args, **kwargs)\n", " self.gamma = self.add_weight(name='gamma', shape=[], initializer='ones', trainable=True)\n", "\n", " def build(self, input_shape):\n", " \"\"\"Builds the layer, initializing weights.\"\"\"\n", " super(SpectralNormalizedAttention, self).build(input_shape)\n", " # Additional initializations can be added here if necessary.\n", "\n", " def _normalize_weights(self, W):\n", " \"\"\"\n", " Normalizes the weights matrix W using its spectral norm.\n", " \n", " Parameters:\n", " W (tf.Tensor): The weight matrix to normalize.\n", " \n", " Returns:\n", " tf.Tensor: Spectrally normalized weights.\n", " \"\"\"\n", " singular_values = tf.linalg.svd(W, compute_uv=False)\n", " spectral_norm = tf.reduce_max(singular_values)\n", " return W / spectral_norm\n", "\n", " def call(self, query, value, key=None, attention_mask=None, return_attention_scores=False):\n", " \"\"\"\n", " Calls the SpectralNormalizedAttention layer. Normalizes the query, key, and value weights\n", " before calling the parent MultiHeadAttention layer.\n", " \n", " Parameters:\n", " query (tf.Tensor): Query tensor.\n", " value (tf.Tensor): Value tensor.\n", " key (tf.Tensor): Key tensor. Defaults to None, in which case the query is used as the key.\n", " attention_mask (tf.Tensor): Optional tensor to mask out certain positions from attending to others.\n", " return_attention_scores (bool): Flag to return attention scores along with output.\n", " \n", " Returns:\n", " A tuple of (output tensor, attention scores) if return_attention_scores is True, otherwise just the output tensor.\n", " \"\"\"\n", " key = query if key is None else key\n", " query = self._normalize_weights(query) * self.gamma\n", " key = self._normalize_weights(key) * self.gamma\n", " value = self._normalize_weights(value) * self.gamma\n", "\n", " return super(SpectralNormalizedAttention, self).call(query, value, key, attention_mask, return_attention_scores)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "663d4c7e", "metadata": {}, "outputs": [], "source": [ "class BaseModel(tf.keras.Model):\n", " \"\"\"\n", " A base model class that integrates various enhancements including \n", " Reversible Instance Normalization and Channel-Wise Attention, and optionally,\n", " spectral normalization and SAM optimization. To use SAMformer, enable use_sam,\n", " use_attention, use_revin and trainable.\n", " \n", " Attributes:\n", " pred_len (int): The length of the output predictions.\n", " num_heads (int): The number of heads in the multi-head attention mechanism. \n", " d_model (int): The dimensionality of the embedding vectors.\n", " use_sam (bool): If True, applies Sharpness-Aware Minimization (SAM) optimization technique during training, \n", " aiming to improve model generalization by considering the loss landscape's sharpness.\n", " use_attention (bool): If True, enables the multi-head attention mechanism in the model. If False, the model is\n", " equivalent to a simple linear layer.\n", " use_revin (bool): If True, applies Reversible Instance Normalization (RevIN) to the model.\n", " trainable (bool): Specifies if the model's weights should be updated or frozen during training. Useful to \n", " highlight some attention layer issues in Time Series Forecasting.\n", " rho (float): The neighborhood size parameter for SAM optimization. It determines the radius within which SAM \n", " seeks to minimize the sharpness of the loss landscape.\n", " spec (bool): If True, applies spectral normalization (sigma-reparam) to the attention mechanism, aiming to\n", " stabilize the training by constraining the spectral norm of the weight matrices.\n", " \n", " Methods:\n", " call(inputs, training=False): Defines the computation from inputs to outputs, optionally applying SAM, \n", " spectral normalization, and reversible instance normalization based on the \n", " configuration.\n", " _apply_attention(x): Applies the attention mechanism to the input tensor, capturing the inter-dependencies \n", " within the data thanks to the Channel-Wise Attention mechanism.\n", " get_last_attention_weights(): Retrieves the attention weights from the last but one batch, useful for \n", " analysis and debugging purposes.\n", " train_step(data): Custom training logic, including the application of SAM's two-step optimization process, \n", " to improve model generalization and performance stability.\n", "\n", " \"\"\"\n", "\n", " def __init__(self, pred_len, num_heads=1, d_model=16, use_sam=None, \n", " use_attention=None, use_revin=None, \n", " trainable=None, rho=None, spec=None):\n", " super(BaseModel, self).__init__()\n", " self.pred_len = pred_len\n", " self.num_heads = num_heads\n", " self.d_model = d_model\n", " self.use_sam = use_sam\n", " self.use_attention = use_attention\n", " self.use_revin = use_revin\n", " self.rho = rho if use_sam and trainable else 0.0\n", " self.spec = spec\n", "\n", " # Define model layers\n", " self.rev_norm = RevNorm(axis=-2)\n", " self.attention_layer = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)\n", " self.dense = layers.Dense(pred_len)\n", " self.all_attention_weights = collections.deque(maxlen=2)\n", " self.all_dense_weights = collections.deque(maxlen=2)\n", "\n", " if self.spec:\n", " self.spec_layer = SpectralNormalizedAttention(num_heads=num_heads, key_dim=d_model)\n", "\n", " #Define trainability of attention layer\n", " self.attention_layer.trainable = trainable\n", "\n", " def call(self, inputs, training=False):\n", " \"\"\"\n", " The forward pass for the model.\n", " \n", " Parameters:\n", " inputs (Tensor): Input tensor.\n", " training (bool): Whether the call is for training.\n", " \n", " Returns:\n", " Tensor: The output of the model.\n", " \"\"\"\n", "\n", " x = inputs\n", " if self.use_revin:\n", " x = self.rev_norm(x, mode='norm')\n", " x = tf.transpose(x, perm=[0, 2, 1])\n", "\n", " if self.use_attention:\n", " attention_output = self._apply_attention(x)\n", " x = layers.Add()([x, attention_output])\n", "\n", " x = self.dense(x)\n", " outputs = tf.transpose(x, perm=[0, 2, 1])\n", "\n", " if self.use_revin:\n", " outputs = self.rev_norm(outputs, mode='denorm')\n", "\n", " return outputs\n", "\n", " def _apply_attention(self, x):\n", " \"\"\"\n", " Applies the attention mechanism to the input tensor.\n", " \n", " Parameters:\n", " x (Tensor): The input tensor.\n", " training (bool): Whether the call is for training.\n", " \n", " Returns:\n", " Tensor: The output tensor after applying attention.\n", " \"\"\"\n", " if self.spec:\n", " attention_output, weights = self.spec_layer(x, x, return_attention_scores=True)\n", " else:\n", " attention_output, weights = self.attention_layer(x, x, return_attention_scores=True)\n", " \n", " self.all_attention_weights.append(weights.numpy())\n", " return attention_output\n", "\n", " def get_last_attention_weights(self):\n", " \"\"\"Returns the attention weights from the last but one batch.\"\"\"\n", " if len(self.all_attention_weights) > 1:\n", " return self.all_attention_weights[-2]\n", " return None\n", "\n", " def train_step(self, data):\n", " sam_optimizer = SAM(self.optimizer, rho=self.rho, eps=1e-12) \n", "\n", " # Unpack the data.\n", " x, y = data\n", "\n", " with tf.GradientTape() as tape:\n", " y_pred = self(x, training=True) # Forward pass\n", " loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)\n", "\n", " # Compute gradients\n", " gradients = tape.gradient(loss, self.trainable_variables)\n", "\n", " # Apply SAM's first step\n", " sam_optimizer.first_step(gradients, self.trainable_variables)\n", "\n", " with tf.GradientTape() as tape:\n", " y_pred = self(x, training=True) # Forward pass again\n", " loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)\n", "\n", " # Compute gradients again\n", " gradients = tape.gradient(loss, self.trainable_variables)\n", "\n", " # Apply SAM's second step\n", " sam_optimizer.second_step(gradients, self.trainable_variables)\n", "\n", " # Update metrics\n", " self.compiled_metrics.update_state(y, y_pred)\n", "\n", " # Return a dict mapping metric names to current value\n", " return {m.name: m.result() for m in self.metrics}\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "cdc39fe9", "metadata": {}, "outputs": [], "source": [ "class TSFDataLoader:\n", " \"\"\"Generate data loader from raw data.\"\"\"\n", "\n", " def __init__(\n", " self, data, batch_size, seq_len, pred_len, feature_type, target='OT'\n", " ):\n", " self.data = data\n", " self.batch_size = batch_size\n", " self.seq_len = seq_len\n", " self.pred_len = pred_len\n", " self.feature_type = feature_type\n", " self.target = target\n", " self.target_slice = slice(0, None)\n", "\n", " self._read_data()\n", "\n", " def _read_data(self):\n", " \"\"\"Load raw data and split datasets.\"\"\"\n", "\n", " # copy data from cloud storage if not exists\n", " if not os.path.isdir(LOCAL_CACHE_DIR):\n", " os.mkdir(LOCAL_CACHE_DIR)\n", "\n", " file_name = self.data + '.csv'\n", " cache_filepath = os.path.join(LOCAL_CACHE_DIR, file_name)\n", " if not os.path.isfile(cache_filepath):\n", " tf.io.gfile.copy(\n", " os.path.join(DATA_DIR, file_name), cache_filepath, overwrite=True\n", " )\n", " df_raw = pd.read_csv(cache_filepath)\n", "\n", " \n", " \n", " # S: univariate-univariate, M: multivariate-multivariate, MS:\n", " # multivariate-univariate\n", " df = df_raw.set_index('date')\n", " if self.feature_type == 'S':\n", " df = df[[self.target]]\n", " elif self.feature_type == 'MS':\n", " target_idx = df.columns.get_loc(self.target)\n", " self.target_slice = slice(target_idx, target_idx + 1)\n", "\n", " # split train/valid/test\n", " n = len(df)\n", " if self.data.startswith('ETTm'):\n", " train_end = 12 * 30 * 24 * 4\n", " val_end = train_end + 4 * 30 * 24 * 4\n", " test_end = val_end + 4 * 30 * 24 * 4\n", " elif self.data.startswith('ETTh'):\n", " train_end = 12 * 30 * 24\n", " val_end = train_end + 4 * 30 * 24\n", " test_end = val_end + 4 * 30 * 24\n", " else:\n", " train_end = int(n * 0.7)\n", " val_end = n - int(n * 0.2)\n", " test_end = n\n", " \n", " train_df = df[:train_end]\n", " val_df = df[train_end - self.seq_len : val_end]\n", " test_df = df[val_end - self.seq_len : test_end]\n", "\n", " # standardize by training set\n", " self.scaler = StandardScaler()\n", " self.scaler1 = StandardScaler()\n", " \n", " self.scaler.fit(train_df.values)\n", " # self.scaler1.fit(df.iloc[:train_end, :-5].values)\n", "\n", " def scale_df(df, scaler):\n", " data = scaler.transform(df.values)\n", " # return pd.DataFrame(df, index=df.index, columns=df.columns)\n", " return pd.DataFrame(data, index=df.index, columns=df.columns)\n", "\n", " self.train_df = scale_df(train_df, self.scaler)\n", " self.val_df = scale_df(val_df, self.scaler)\n", " self.test_df = scale_df(test_df, self.scaler)\n", " self.n_feature = self.train_df.shape[-1]\n", "\n", " def _split_window(self, data):\n", " inputs = data[:, : self.seq_len, :]\n", " labels = data[:, self.seq_len :, self.target_slice]\n", " # Slicing doesn't preserve static shape information, so set the shapes\n", " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", " inputs.set_shape([None, self.seq_len, None])\n", " labels.set_shape([None, self.pred_len, self.n_feature])\n", " return inputs, labels\n", "\n", " def _make_dataset(self, data, shuffle=True):\n", " data = np.array(data, dtype=np.float32)\n", " ds = tf.keras.utils.timeseries_dataset_from_array(\n", " data=data,\n", " targets=None,\n", " sequence_length=(self.seq_len + self.pred_len),\n", " sequence_stride=1,\n", " shuffle=False,\n", " batch_size=self.batch_size,\n", " )\n", " ds = ds.map(self._split_window)\n", " return ds\n", "\n", " def _make_dataset_test(self, data, shuffle=True):\n", " data = np.array(data, dtype=np.float32)\n", " ds = tf.keras.utils.timeseries_dataset_from_array(\n", " data=data,\n", " targets=None,\n", " sequence_length=(self.seq_len + self.pred_len),\n", " sequence_stride=1,\n", " shuffle=False,\n", " batch_size=1)\n", " ds = ds.map(self._split_window)\n", " return ds\n", "\n", "\n", " def inverse_transform(self, data):\n", " return self.scaler.inverse_transform(data)\n", "\n", " def get_train(self, shuffle=True):\n", " return self._make_dataset(self.train_df, shuffle=shuffle)\n", "\n", " def get_val(self):\n", " return self._make_dataset(self.val_df, shuffle=False)\n", "\n", " def get_test(self):\n", " return self._make_dataset(self.test_df, shuffle=False)\n", "\n", "\n", "def load_data(name, batch_size, seq_len, pred_len, feature_type, target):\n", " \"\"\"\n", " Loads or generates training, validation, and testing datasets based on the specified configurations.\n", "\n", " Parameters:\n", " args (argparse.Namespace): Command line arguments specifying dataset configurations.\n", "\n", " Returns:\n", " tuple: Training, validation, and test datasets as tf.data.Dataset objects.\n", " \"\"\"\n", " data_loader = TSFDataLoader(name, batch_size, seq_len, pred_len, feature_type, target)\n", " train_data, val_data, test_data = data_loader.get_train(), data_loader.get_val(), data_loader.get_test()\n", "\n", " return train_data, val_data, test_data, data_loader.n_feature" ] }, { "cell_type": "code", "execution_count": null, "id": "fe7abdc1", "metadata": {}, "outputs": [], "source": [ "train_data, val_data, test_data, n_features = load_data(name, batch_size, seq_len, pred_len, feature_type, target)\n", "new_cols = ['mse', 'mae', \"seq_len\", \"learning_rate\", \"batch_size\", \"num_heads\", \n", " \"d_model\", \"minimization_neighbourhood\"]\n", "\n", "df = pd.DataFrame(columns=new_cols)\n", "\n", "numEvalSamples = 1\n", "seeds = [2022]" ] }, { "cell_type": "code", "execution_count": null, "id": "29470575", "metadata": {}, "outputs": [], "source": [ "for sd in range(numEvalSamples):\n", " tf.keras.backend.clear_session() \n", "\n", " tf.keras.utils.set_random_seed(seeds[sd])\n", " \n", " print(f\"..............iteration{sd}.................\")\n", " print()\n", " model = BaseModel(pred_len, num_heads=num_heads, d_model=d_model, use_sam=True, use_attention=True, use_revin=True, trainable=True, rho=rho, \n", " spec=False)\n", " optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n", " model.compile(optimizer=optimizer, loss='mae', metrics=['mse', 'mae'], run_eagerly=True)\n", " \n", " callbacks, capture_weights_callback = setup_callbacks(learning_rate, patience, checkpoint_path, model)\n", " \n", " history = model.fit(\n", " train_data,\n", " epochs=300,\n", " validation_data=val_data,\n", " callbacks=callbacks)\n", " \n", " model.load_weights(checkpoint_path)\n", " df.loc[sd, :] = model.evaluate(test_data)[-2:] + [ seq_len, learning_rate, batch_size, num_heads, \n", " d_model, rho]\n", " df.to_csv(name_df, header=True, index=False)\n", "\n", " del model\n", " tf.keras.backend.clear_session()\n", " # garbage collector\n", " gc.collect()\n", "\n", " # remove_dir(checkpoint_path)\n", "\n", "print(\"................completed Successfully......................\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "852cd02e", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }