From ad1ac90cd388f29af98f54884db4d794004f4792 Mon Sep 17 00:00:00 2001 From: Humbulani Date: Wed, 8 Jan 2025 13:18:31 +0200 Subject: [PATCH 1/6] adapting the script classification_with_grn_and_vsn to be backend-agnostic --- .../classification_with_grn_and_vsn.py | 235 +- .../classification_with_grn_and_vsn.ipynb | 309 +- .../md/classification_with_grn_and_vsn.md | 3504 ++++++++++++++++- 3 files changed, 3780 insertions(+), 268 deletions(-) diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index e0d4cde613..809f07fa23 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -2,9 +2,10 @@ Title: Classification with Gated Residual and Variable Selection Networks Author: [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/) Date created: 2021/02/10 -Last modified: 2021/02/10 +Last modified: 2025/01/08 Description: Using Gated Residual and Variable Selection Networks for income level prediction. Accelerator: GPU +Converted to Keras 3 by: [Sitam Meur](https://github.com/sitamgithub-MSIT) and made backend-agnostic by: [Humbulani Ndou](https://github.com/Humbulani1234) """ """ @@ -46,13 +47,13 @@ """ import os +import subprocess +import tarfile -# Only the TensorFlow backend supports string inputs. -os.environ["KERAS_BACKEND"] = "tensorflow" +os.environ["KERAS_BACKEND"] = "torch" # or jax, or tensorflow import numpy as np import pandas as pd -import tensorflow as tf import keras from keras import layers @@ -108,13 +109,37 @@ "income_level", ] -data_url = "https://archive.ics.uci.edu/static/public/20/census+income.zip" +data_url = "https://archive.ics.uci.edu/static/public/117/census+income+kdd.zip" keras.utils.get_file(origin=data_url, extract=True) + +""" +Determine the downloaded .tar.gz file path and +extract the files from the downloaded .tar.gz file +""" + +extracted_path = os.path.join( + os.path.expanduser("~"), ".keras", "datasets", "census+income+kdd.zip" +) +for root, dirs, files in os.walk(extracted_path): + for file in files: + if file.endswith(".tar.gz"): + tar_gz_path = os.path.join(root, file) + with tarfile.open(tar_gz_path, "r:gz") as tar: + tar.extractall(path=root) + train_data_path = os.path.join( - os.path.expanduser("~"), ".keras", "datasets", "adult.data" + os.path.expanduser("~"), + ".keras", + "datasets", + "census+income+kdd.zip", + "census-income.data", ) test_data_path = os.path.join( - os.path.expanduser("~"), ".keras", "datasets", "adult.test" + os.path.expanduser("~"), + ".keras", + "datasets", + "census+income+kdd.zip", + "census-income.test", ) data = pd.read_csv(train_data_path, header=None, names=CSV_HEADER) @@ -157,6 +182,20 @@ valid_data.to_csv(valid_data_file, index=False, header=False) test_data.to_csv(test_data_file, index=False, header=False) +""" +Clean the directory for the downloaded files except the .tar.gz file and +also remove the empty directories +""" + +subprocess.run( + f'find {extracted_path} -type f ! -name "*.tar.gz" -exec rm -f {{}} +', + shell=True, + check=True, +) +subprocess.run( + f"find {extracted_path} -type d -empty -exec rmdir {{}} +", shell=True, check=True +) + """ ## Define dataset metadata @@ -211,15 +250,38 @@ training and evaluation. """ +# Tensorflow required for tf.data.Datasets +import tensorflow as tf + +# We process our datasets elements here (categorical) and convert them to indices to avoid this step +# during model training since only tensorflow support strings. def process(features, target): for feature_name in features: if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: # Cast categorical feature values to string. - features[feature_name] = keras.ops.cast(features[feature_name], "string") + features[feature_name] = tf.cast(features[feature_name], "string") + vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name] + # Create a lookup to convert a string values to an integer indices. + # Since we are not using a mask token nor expecting any out of vocabulary + # (oov) token, we set mask_token to None and num_oov_indices to 0. + index = layers.StringLookup( + vocabulary=vocabulary, + mask_token=None, + num_oov_indices=0, + output_mode="int", + ) + # Convert the string input values into integer indices. + value_index = index(features[feature_name]) + features[feature_name] = value_index + else: + # Do nothing for numerical features + pass + # Get the instance weight. weight = features.pop(WEIGHT_COLUMN_NAME) - return features, target, weight + # Change features from OrderedDict to Dict to match Inputs as they are Dict. + return dict(features), target, weight def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128): @@ -245,56 +307,19 @@ def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128): def create_model_inputs(): inputs = {} for feature_name in FEATURE_NAMES: - if feature_name in NUMERIC_FEATURE_NAMES: + if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: + # Make them int64, they are Categorical (whole units) inputs[feature_name] = layers.Input( - name=feature_name, shape=(), dtype="float32" + name=feature_name, shape=(), dtype="int64" ) else: + # Make them float32, they are Real numbers inputs[feature_name] = layers.Input( - name=feature_name, shape=(), dtype="string" + name=feature_name, shape=(), dtype="float32" ) return inputs -""" -## Encode input features - -For categorical features, we encode them using `layers.Embedding` using the -`encoding_size` as the embedding dimensions. For the numerical features, -we apply linear transformation using `layers.Dense` to project each feature into -`encoding_size`-dimensional vector. Thus, all the encoded features will have the -same dimensionality. - -""" - - -def encode_inputs(inputs, encoding_size): - encoded_features = [] - for feature_name in inputs: - if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: - vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name] - # Create a lookup to convert a string values to an integer indices. - # Since we are not using a mask token nor expecting any out of vocabulary - # (oov) token, we set mask_token to None and num_oov_indices to 0. - index = layers.StringLookup( - vocabulary=vocabulary, mask_token=None, num_oov_indices=0 - ) - # Convert the string input values into integer indices. - value_index = index(inputs[feature_name]) - # Create an embedding layer with the specified dimensions - embedding_ecoder = layers.Embedding( - input_dim=len(vocabulary), output_dim=encoding_size - ) - # Convert the index values to embedding representations. - encoded_feature = embedding_ecoder(value_index) - else: - # Project the numeric feature to encoding_size using linear transformation. - encoded_feature = keras.ops.expand_dims(inputs[feature_name], -1) - encoded_feature = layers.Dense(units=encoding_size)(encoded_feature) - encoded_features.append(encoded_feature) - return encoded_features - - """ ## Implement the Gated Linear Unit @@ -360,12 +385,20 @@ def call(self, inputs): Note that the output of the VSN is [batch_size, encoding_size], regardless of the number of the input features. + +For categorical features, we encode them using `layers.Embedding` using the +`encoding_size` as the embedding dimensions. For the numerical features, +we apply linear transformation using `layers.Dense` to project each feature into +`encoding_size`-dimensional vector. Thus, all the encoded features will have the +same dimensionality. + """ class VariableSelection(layers.Layer): def __init__(self, num_features, units, dropout_rate): super().__init__() + self.units = units self.grns = list() # Create a GRN for each feature independently for idx in range(num_features): @@ -376,17 +409,88 @@ def __init__(self, num_features, units, dropout_rate): self.softmax = layers.Dense(units=num_features, activation="softmax") def call(self, inputs): - v = layers.concatenate(inputs) + concat_inputs = [] + for input_ in inputs: + if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY: + vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_] + # Create an embedding layer with the specified dimensions + embedding_encoder = layers.Embedding( + input_dim=len(vocabulary), output_dim=self.units + ) + max_index = len(vocabulary) - 1 # Clamp the indices + # torch had some index errors during embedding hence the clip function + embedded_feature = embedding_encoder( + keras.ops.clip(inputs[input_], 0, max_index) + ) + concat_inputs.append(embedded_feature) + else: + # Project the numeric feature to encoding_size using linear transformation. + proj_feature = keras.ops.expand_dims(inputs[input_], -1) + proj_feature = layers.Dense(units=self.units)(proj_feature) + concat_inputs.append(proj_feature) + + v = layers.concatenate(concat_inputs) v = self.grn_concat(v) v = keras.ops.expand_dims(self.softmax(v), axis=-1) - x = [] - for idx, input in enumerate(inputs): + for idx, input in enumerate(concat_inputs): x.append(self.grns[idx](input)) x = keras.ops.stack(x, axis=1) - outputs = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) - return outputs + # The reason for each individual backend calculation is that I couldn't find + # the equivalent keras operation that is backend-agnostic. In the following case there's + # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul + # for all backends, but due to jax jit tracing it results in an error. + + def matmul_dependent_on_backend(tensor_1, tensor_2): + """ + Function for executing matmul for each backend. + """ + # jax backend + if keras.backend.backend() == "jax": + import jax.numpy as jnp + + result = jnp.sum(tensor_1 * tensor_2, axis=1) + elif keras.backend.backend() == "torch": + import torch + + result = torch.sum(tensor_1 * tensor_2, dim=1) + # tensorflow backend + elif keras.backend.backend() == "tensorflow": + import tensorflow as tf + + result = keras.ops.squeeze( + tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1 + ) + # unsupported backend exception + else: + raise ValueError( + "Unsupported backend: {}".format(keras.backend.backend()) + ) + return result + + # jax backend + if keras.backend.backend() == "jax": + # This repetative imports are intentional to force the idea of backend + # separation + import jax.numpy as jnp + + result_jax = matmul_dependent_on_backend(v, x) + return result_jax + + # torch backend + if keras.backend.backend() == "torch": + import torch + + result_torch = matmul_dependent_on_backend(v, x) + return result_torch + + # tensorflow backend + if keras.backend.backend() == "tensorflow": + import tensorflow as tf + + result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) + return result_tf """ @@ -396,14 +500,10 @@ def call(self, inputs): def create_model(encoding_size): inputs = create_model_inputs() - feature_list = encode_inputs(inputs, encoding_size) - num_features = len(feature_list) - - features = VariableSelection(num_features, encoding_size, dropout_rate)( - feature_list - ) - + num_features = len(inputs) + features = VariableSelection(num_features, encoding_size, dropout_rate)(inputs) outputs = layers.Dense(units=1, activation="sigmoid")(features) + # Functional model model = keras.Model(inputs=inputs, outputs=outputs) return model @@ -415,7 +515,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 20 +num_epochs = 1 # maybe adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) @@ -425,6 +525,13 @@ def create_model(encoding_size): metrics=[keras.metrics.BinaryAccuracy(name="accuracy")], ) +""" +Let's visualize our connectivity graph: +""" + +# `rankdir='LR'` is to make the graph horizontal. +keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, rankdir="LR") + # Create an early stopping callback. early_stopping = keras.callbacks.EarlyStopping( diff --git a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb index f1686e45d1..147943e8b1 100644 --- a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb +++ b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb @@ -10,7 +10,7 @@ "\n", "**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)
\n", "**Date created:** 2021/02/10
\n", - "**Last modified:** 2021/02/10
\n", + "**Last modified:** 2025/01/08
\n", "**Description:** Using Gated Residual and Variable Selection Networks for income level prediction." ] }, @@ -76,13 +76,13 @@ "outputs": [], "source": [ "import os\n", + "import subprocess\n", + "import tarfile\n", "\n", - "# Only the TensorFlow backend supports string inputs.\n", - "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\" # or jax, or tensorflow\n", "\n", "import numpy as np\n", "import pandas as pd\n", - "import tensorflow as tf\n", "import keras\n", "from keras import layers" ] @@ -152,11 +152,55 @@ " \"income_level\",\n", "]\n", "\n", - "data_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census-income.data.gz\"\n", - "data = pd.read_csv(data_url, header=None, names=CSV_HEADER)\n", + "data_url = \"https://archive.ics.uci.edu/static/public/117/census+income+kdd.zip\"\n", + "keras.utils.get_file(origin=data_url, extract=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "Determine the downloaded .tar.gz file path and\n", + "extract the files from the downloaded .tar.gz file" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "extracted_path = os.path.join(\n", + " os.path.expanduser(\"~\"), \".keras\", \"datasets\", \"census+income+kdd.zip\"\n", + ")\n", + "for root, dirs, files in os.walk(extracted_path):\n", + " for file in files:\n", + " if file.endswith(\".tar.gz\"):\n", + " tar_gz_path = os.path.join(root, file)\n", + " with tarfile.open(tar_gz_path, \"r:gz\") as tar:\n", + " tar.extractall(path=root)\n", + "\n", + "train_data_path = os.path.join(\n", + " os.path.expanduser(\"~\"),\n", + " \".keras\",\n", + " \"datasets\",\n", + " \"census+income+kdd.zip\",\n", + " \"census-income.data\",\n", + ")\n", + "test_data_path = os.path.join(\n", + " os.path.expanduser(\"~\"),\n", + " \".keras\",\n", + " \"datasets\",\n", + " \"census+income+kdd.zip\",\n", + " \"census-income.test\",\n", + ")\n", "\n", - "test_data_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census-income.test.gz\"\n", - "test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER)\n", + "data = pd.read_csv(train_data_path, header=None, names=CSV_HEADER)\n", + "test_data = pd.read_csv(test_data_path, header=None, names=CSV_HEADER)\n", "\n", "print(f\"Data shape: {data.shape}\")\n", "print(f\"Test data shape: {test_data.shape}\")\n", @@ -238,6 +282,34 @@ "test_data.to_csv(test_data_file, index=False, header=False)" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "Clean the directory for the downloaded files except the .tar.gz file and\n", + "also remove the empty directories" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "subprocess.run(\n", + " f'find {extracted_path} -type f ! -name \"*.tar.gz\" -exec rm -f {{}} +',\n", + " shell=True,\n", + " check=True,\n", + ")\n", + "subprocess.run(\n", + " f\"find {extracted_path} -type d -empty -exec rmdir {{}} +\", shell=True, check=True\n", + ")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -288,9 +360,12 @@ ")\n", "# Feature default values.\n", "COLUMN_DEFAULTS = [\n", - " [0.0]\n", - " if feature_name in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME]\n", - " else [\"NA\"]\n", + " (\n", + " [0.0]\n", + " if feature_name\n", + " in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME]\n", + " else [\"NA\"]\n", + " )\n", " for feature_name in CSV_HEADER\n", "]" ] @@ -316,15 +391,38 @@ }, "outputs": [], "source": [ + "# Tensorflow required for tf.data.Datasets\n", + "import tensorflow as tf\n", "\n", + "\n", + "# We process our datasets elements here (categorical) and convert them to indices to avoid this step\n", + "# during model training since only tensorflow support strings.\n", "def process(features, target):\n", " for feature_name in features:\n", " if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n", " # Cast categorical feature values to string.\n", - " features[feature_name] = keras.ops.cast(features[feature_name], \"string\")\n", + " features[feature_name] = tf.cast(features[feature_name], \"string\")\n", + " vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n", + " # Create a lookup to convert a string values to an integer indices.\n", + " # Since we are not using a mask token nor expecting any out of vocabulary\n", + " # (oov) token, we set mask_token to None and num_oov_indices to 0.\n", + " index = layers.StringLookup(\n", + " vocabulary=vocabulary,\n", + " mask_token=None,\n", + " num_oov_indices=0,\n", + " output_mode=\"int\",\n", + " )\n", + " # Convert the string input values into integer indices.\n", + " value_index = index(features[feature_name])\n", + " features[feature_name] = value_index\n", + " else:\n", + " # Do nothing for numerical features\n", + " pass\n", + "\n", " # Get the instance weight.\n", " weight = features.pop(WEIGHT_COLUMN_NAME)\n", - " return features, target, weight\n", + " # Change features from OrderedDict to Dict to match Inputs as they are Dict.\n", + " return dict(features), target, weight\n", "\n", "\n", "def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128):\n", @@ -364,70 +462,20 @@ "def create_model_inputs():\n", " inputs = {}\n", " for feature_name in FEATURE_NAMES:\n", - " if feature_name in NUMERIC_FEATURE_NAMES:\n", + " if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n", + " # Make them int64, they are Categorical (whole units)\n", " inputs[feature_name] = layers.Input(\n", - " name=feature_name, shape=(), dtype=\"float32\"\n", + " name=feature_name, shape=(), dtype=\"int64\"\n", " )\n", " else:\n", + " # Make them float32, they are Real numbers\n", " inputs[feature_name] = layers.Input(\n", - " name=feature_name, shape=(), dtype=\"string\"\n", + " name=feature_name, shape=(), dtype=\"float32\"\n", " )\n", " return inputs\n", "" ] }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text" - }, - "source": [ - "## Encode input features\n", - "\n", - "For categorical features, we encode them using `layers.Embedding` using the\n", - "`encoding_size` as the embedding dimensions. For the numerical features,\n", - "we apply linear transformation using `layers.Dense` to project each feature into\n", - "`encoding_size`-dimensional vector. Thus, all the encoded features will have the\n", - "same dimensionality." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab_type": "code" - }, - "outputs": [], - "source": [ - "\n", - "def encode_inputs(inputs, encoding_size):\n", - " encoded_features = []\n", - " for feature_name in inputs:\n", - " if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n", - " vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n", - " # Create a lookup to convert a string values to an integer indices.\n", - " # Since we are not using a mask token nor expecting any out of vocabulary\n", - " # (oov) token, we set mask_token to None and num_oov_indices to 0.\n", - " index = layers.StringLookup(\n", - " vocabulary=vocabulary, mask_token=None, num_oov_indices=0\n", - " )\n", - " # Convert the string input values into integer indices.\n", - " value_index = index(inputs[feature_name])\n", - " # Create an embedding layer with the specified dimensions\n", - " embedding_ecoder = layers.Embedding(\n", - " input_dim=len(vocabulary), output_dim=encoding_size\n", - " )\n", - " # Convert the index values to embedding representations.\n", - " encoded_feature = embedding_ecoder(value_index)\n", - " else:\n", - " # Project the numeric feature to encoding_size using linear transformation.\n", - " encoded_feature = keras.ops.expand_dims(inputs[feature_name], -1)\n", - " encoded_feature = layers.Dense(units=encoding_size)(encoded_feature)\n", - " encoded_features.append(encoded_feature)\n", - " return encoded_features\n", - "" - ] - }, { "cell_type": "markdown", "metadata": { @@ -525,7 +573,13 @@ "3. Produces a weighted sum of the output of the individual GRN.\n", "\n", "Note that the output of the VSN is [batch_size, encoding_size], regardless of the\n", - "number of the input features." + "number of the input features.\n", + "\n", + "For categorical features, we encode them using `layers.Embedding` using the\n", + "`encoding_size` as the embedding dimensions. For the numerical features,\n", + "we apply linear transformation using `layers.Dense` to project each feature into\n", + "`encoding_size`-dimensional vector. Thus, all the encoded features will have the\n", + "same dimensionality." ] }, { @@ -540,6 +594,7 @@ "class VariableSelection(layers.Layer):\n", " def __init__(self, num_features, units, dropout_rate):\n", " super().__init__()\n", + " self.units = units\n", " self.grns = list()\n", " # Create a GRN for each feature independently\n", " for idx in range(num_features):\n", @@ -550,17 +605,88 @@ " self.softmax = layers.Dense(units=num_features, activation=\"softmax\")\n", "\n", " def call(self, inputs):\n", - " v = layers.concatenate(inputs)\n", + " concat_inputs = []\n", + " for input_ in inputs:\n", + " if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n", + " vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_]\n", + " # Create an embedding layer with the specified dimensions\n", + " embedding_encoder = layers.Embedding(\n", + " input_dim=len(vocabulary), output_dim=self.units\n", + " )\n", + " max_index = len(vocabulary) - 1 # Clamp the indices\n", + " # torch had some index errors during embedding hence the clip function\n", + " embedded_feature = embedding_encoder(\n", + " keras.ops.clip(inputs[input_], 0, max_index)\n", + " )\n", + " concat_inputs.append(embedded_feature)\n", + " else:\n", + " # Project the numeric feature to encoding_size using linear transformation.\n", + " proj_feature = keras.ops.expand_dims(inputs[input_], -1)\n", + " proj_feature = layers.Dense(units=self.units)(proj_feature)\n", + " concat_inputs.append(proj_feature)\n", + "\n", + " v = layers.concatenate(concat_inputs)\n", " v = self.grn_concat(v)\n", " v = keras.ops.expand_dims(self.softmax(v), axis=-1)\n", - "\n", " x = []\n", - " for idx, input in enumerate(inputs):\n", + " for idx, input in enumerate(concat_inputs):\n", " x.append(self.grns[idx](input))\n", " x = keras.ops.stack(x, axis=1)\n", "\n", - " outputs = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)\n", - " return outputs\n", + " # The reason for each individual backend calculation is that I couldn't find\n", + " # the equivalent keras operation that is backend-agnostic. In the following case there's\n", + " # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul\n", + " # for all backends, but due to jax jit tracing it results in an error.\n", + "\n", + " def matmul_dependent_on_backend(tensor_1, tensor_2):\n", + " \"\"\"\n", + " Function for executing matmul for each backend.\n", + " \"\"\"\n", + " # jax backend\n", + " if keras.backend.backend() == \"jax\":\n", + " import jax.numpy as jnp\n", + "\n", + " result = jnp.sum(tensor_1 * tensor_2, axis=1)\n", + " elif keras.backend.backend() == \"torch\":\n", + " import torch\n", + "\n", + " result = torch.sum(tensor_1 * tensor_2, dim=1)\n", + " # tensorflow backend\n", + " elif keras.backend.backend() == \"tensorflow\":\n", + " import tensorflow as tf\n", + "\n", + " result = keras.ops.squeeze(\n", + " tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1\n", + " )\n", + " # unsupported backend exception\n", + " else:\n", + " raise ValueError(\n", + " \"Unsupported backend: {}\".format(keras.backend.backend())\n", + " )\n", + " return result\n", + "\n", + " # jax backend\n", + " if keras.backend.backend() == \"jax\":\n", + " # This repetative imports are intentional to force the idea of backend\n", + " # separation\n", + " import jax.numpy as jnp\n", + "\n", + " result_jax = matmul_dependent_on_backend(v, x)\n", + " return result_jax\n", + "\n", + " # torch backend\n", + " if keras.backend.backend() == \"torch\":\n", + " import torch\n", + "\n", + " result_torch = matmul_dependent_on_backend(v, x)\n", + " return result_torch\n", + "\n", + " # tensorflow backend\n", + " if keras.backend.backend() == \"tensorflow\":\n", + " import tensorflow as tf\n", + "\n", + " result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)\n", + " return result_tf\n", "" ] }, @@ -584,14 +710,10 @@ "\n", "def create_model(encoding_size):\n", " inputs = create_model_inputs()\n", - " feature_list = encode_inputs(inputs, encoding_size)\n", - " num_features = len(feature_list)\n", - "\n", - " features = VariableSelection(num_features, encoding_size, dropout_rate)(\n", - " feature_list\n", - " )\n", - "\n", + " num_features = len(inputs)\n", + " features = VariableSelection(num_features, encoding_size, dropout_rate)(inputs)\n", " outputs = layers.Dense(units=1, activation=\"sigmoid\")(features)\n", + " # Functional model\n", " model = keras.Model(inputs=inputs, outputs=outputs)\n", " return model\n", "" @@ -617,7 +739,7 @@ "learning_rate = 0.001\n", "dropout_rate = 0.15\n", "batch_size = 265\n", - "num_epochs = 20\n", + "num_epochs = 1 # maybe adjusted to a desired value\n", "encoding_size = 16\n", "\n", "model = create_model(encoding_size)\n", @@ -625,7 +747,28 @@ " optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n", " loss=keras.losses.BinaryCrossentropy(),\n", " metrics=[keras.metrics.BinaryAccuracy(name=\"accuracy\")],\n", - ")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "Let's visualize our connectivity graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "# `rankdir='LR'` is to make the graph horizontal.\n", + "keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, rankdir=\"LR\")\n", "\n", "\n", "# Create an early stopping callback.\n", diff --git a/examples/structured_data/md/classification_with_grn_and_vsn.md b/examples/structured_data/md/classification_with_grn_and_vsn.md index e3c9ca9824..5f7ed58c67 100644 --- a/examples/structured_data/md/classification_with_grn_and_vsn.md +++ b/examples/structured_data/md/classification_with_grn_and_vsn.md @@ -2,7 +2,7 @@ **Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)
**Date created:** 2021/02/10
-**Last modified:** 2021/02/10
+**Last modified:** 2025/01/08
**Description:** Using Gated Residual and Variable Selection Networks for income level prediction. @@ -47,12 +47,16 @@ and 34 categorical features. ```python -import math +import os +import subprocess +import tarfile + +os.environ["KERAS_BACKEND"] = "torch" # or jax, or tensorflow + import numpy as np import pandas as pd -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers +import keras +from keras import layers ``` --- @@ -108,11 +112,51 @@ CSV_HEADER = [ "income_level", ] -data_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census-income.data.gz" -data = pd.read_csv(data_url, header=None, names=CSV_HEADER) +data_url = "https://archive.ics.uci.edu/static/public/117/census+income+kdd.zip" +keras.utils.get_file(origin=data_url, extract=True) +``` + + + + +
+``` +'/home/humbulani/.keras/datasets/census+income+kdd.zip' + +``` +
+Determine the downloaded .tar.gz file path and +extract the files from the downloaded .tar.gz file + + +```python +extracted_path = os.path.join( + os.path.expanduser("~"), ".keras", "datasets", "census+income+kdd.zip" +) +for root, dirs, files in os.walk(extracted_path): + for file in files: + if file.endswith(".tar.gz"): + tar_gz_path = os.path.join(root, file) + with tarfile.open(tar_gz_path, "r:gz") as tar: + tar.extractall(path=root) + +train_data_path = os.path.join( + os.path.expanduser("~"), + ".keras", + "datasets", + "census+income+kdd.zip", + "census-income.data", +) +test_data_path = os.path.join( + os.path.expanduser("~"), + ".keras", + "datasets", + "census+income+kdd.zip", + "census-income.test", +) -test_data_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census-income.test.gz" -test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER) +data = pd.read_csv(train_data_path, header=None, names=CSV_HEADER) +test_data = pd.read_csv(test_data_path, header=None, names=CSV_HEADER) print(f"Data shape: {data.shape}") print(f"Test data shape: {test_data.shape}") @@ -162,6 +206,30 @@ valid_data.to_csv(valid_data_file, index=False, header=False) test_data.to_csv(test_data_file, index=False, header=False) ``` +Clean the directory for the downloaded files except the .tar.gz file and +also remove the empty directories + + +```python +subprocess.run( + f'find {extracted_path} -type f ! -name "*.tar.gz" -exec rm -f {{}} +', + shell=True, + check=True, +) +subprocess.run( + f"find {extracted_path} -type d -empty -exec rmdir {{}} +", shell=True, check=True +) +``` + + + + +
+``` +CompletedProcess(args='find /home/humbulani/.keras/datasets/census+income+kdd.zip -type d -empty -exec rmdir {} +', returncode=0) + +``` +
--- ## Define dataset metadata @@ -200,9 +268,12 @@ FEATURE_NAMES = NUMERIC_FEATURE_NAMES + list( ) # Feature default values. COLUMN_DEFAULTS = [ - [0.0] - if feature_name in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME] - else ["NA"] + ( + [0.0] + if feature_name + in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME] + else ["NA"] + ) for feature_name in CSV_HEADER ] ``` @@ -216,21 +287,41 @@ training and evaluation. ```python -from tensorflow.keras.layers import StringLookup +# Tensorflow required for tf.data.Datasets +import tensorflow as tf +# We process our datasets elements here (categorical) and convert them to indices to avoid this step +# during model training since only tensorflow support strings. def process(features, target): for feature_name in features: if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: # Cast categorical feature values to string. - features[feature_name] = tf.cast(features[feature_name], tf.dtypes.string) + features[feature_name] = tf.cast(features[feature_name], "string") + vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name] + # Create a lookup to convert a string values to an integer indices. + # Since we are not using a mask token nor expecting any out of vocabulary + # (oov) token, we set mask_token to None and num_oov_indices to 0. + index = layers.StringLookup( + vocabulary=vocabulary, + mask_token=None, + num_oov_indices=0, + output_mode="int", + ) + # Convert the string input values into integer indices. + value_index = index(features[feature_name]) + features[feature_name] = value_index + else: + # Do nothing for numerical features + pass + # Get the instance weight. weight = features.pop(WEIGHT_COLUMN_NAME) - return features, target, weight + # Change features from OrderedDict to Dict to match Inputs as they are Dict. + return dict(features), target, weight def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128): - dataset = tf.data.experimental.make_csv_dataset( csv_file_path, batch_size=batch_size, @@ -255,58 +346,20 @@ def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128): def create_model_inputs(): inputs = {} for feature_name in FEATURE_NAMES: - if feature_name in NUMERIC_FEATURE_NAMES: + if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: + # Make them int64, they are Categorical (whole units) inputs[feature_name] = layers.Input( - name=feature_name, shape=(), dtype=tf.float32 + name=feature_name, shape=(), dtype="int64" ) else: + # Make them float32, they are Real numbers inputs[feature_name] = layers.Input( - name=feature_name, shape=(), dtype=tf.string + name=feature_name, shape=(), dtype="float32" ) return inputs ``` ---- -## Encode input features - -For categorical features, we encode them using `layers.Embedding` using the -`encoding_size` as the embedding dimensions. For the numerical features, -we apply linear transformation using `layers.Dense` to project each feature into -`encoding_size`-dimensional vector. Thus, all the encoded features will have the -same dimensionality. - - -```python - -def encode_inputs(inputs, encoding_size): - encoded_features = [] - for feature_name in inputs: - if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: - vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name] - # Create a lookup to convert a string values to an integer indices. - # Since we are not using a mask token nor expecting any out of vocabulary - # (oov) token, we set mask_token to None and num_oov_indices to 0. - index = StringLookup( - vocabulary=vocabulary, mask_token=None, num_oov_indices=0 - ) - # Convert the string input values into integer indices. - value_index = index(inputs[feature_name]) - # Create an embedding layer with the specified dimensions - embedding_ecoder = layers.Embedding( - input_dim=len(vocabulary), output_dim=encoding_size - ) - # Convert the index values to embedding representations. - encoded_feature = embedding_ecoder(value_index) - else: - # Project the numeric feature to encoding_size using linear transformation. - encoded_feature = tf.expand_dims(inputs[feature_name], -1) - encoded_feature = layers.Dense(units=encoding_size)(encoded_feature) - encoded_features.append(encoded_feature) - return encoded_features - -``` - --- ## Implement the Gated Linear Unit @@ -377,12 +430,19 @@ produce feature weights. Note that the output of the VSN is [batch_size, encoding_size], regardless of the number of the input features. +For categorical features, we encode them using `layers.Embedding` using the +`encoding_size` as the embedding dimensions. For the numerical features, +we apply linear transformation using `layers.Dense` to project each feature into +`encoding_size`-dimensional vector. Thus, all the encoded features will have the +same dimensionality. + ```python class VariableSelection(layers.Layer): def __init__(self, num_features, units, dropout_rate): super().__init__() + self.units = units self.grns = list() # Create a GRN for each feature independently for idx in range(num_features): @@ -393,17 +453,88 @@ class VariableSelection(layers.Layer): self.softmax = layers.Dense(units=num_features, activation="softmax") def call(self, inputs): - v = layers.concatenate(inputs) + concat_inputs = [] + for input_ in inputs: + if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY: + vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_] + # Create an embedding layer with the specified dimensions + embedding_encoder = layers.Embedding( + input_dim=len(vocabulary), output_dim=self.units + ) + max_index = len(vocabulary) - 1 # Clamp the indices + # torch had some index errors during embedding hence the clip function + embedded_feature = embedding_encoder( + keras.ops.clip(inputs[input_], 0, max_index) + ) + concat_inputs.append(embedded_feature) + else: + # Project the numeric feature to encoding_size using linear transformation. + proj_feature = keras.ops.expand_dims(inputs[input_], -1) + proj_feature = layers.Dense(units=self.units)(proj_feature) + concat_inputs.append(proj_feature) + + v = layers.concatenate(concat_inputs) v = self.grn_concat(v) - v = tf.expand_dims(self.softmax(v), axis=-1) - + v = keras.ops.expand_dims(self.softmax(v), axis=-1) x = [] - for idx, input in enumerate(inputs): + for idx, input in enumerate(concat_inputs): x.append(self.grns[idx](input)) - x = tf.stack(x, axis=1) - - outputs = tf.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) - return outputs + x = keras.ops.stack(x, axis=1) + + # The reason for each individual backend calculation is that I couldn't find + # the equivalent keras operation that is backend-agnostic. In the following case there's + # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul + # for all backends, but due to jax jit tracing it results in an error. + + def matmul_dependent_on_backend(tensor_1, tensor_2): + """ + Function for executing matmul for each backend. + """ + # jax backend + if keras.backend.backend() == "jax": + import jax.numpy as jnp + + result = jnp.sum(tensor_1 * tensor_2, axis=1) + elif keras.backend.backend() == "torch": + import torch + + result = torch.sum(tensor_1 * tensor_2, dim=1) + # tensorflow backend + elif keras.backend.backend() == "tensorflow": + import tensorflow as tf + + result = keras.ops.squeeze( + tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1 + ) + # unsupported backend exception + else: + raise ValueError( + "Unsupported backend: {}".format(keras.backend.backend()) + ) + return result + + # jax backend + if keras.backend.backend() == "jax": + # This repetative imports are intentional to force the idea of backend + # separation + import jax.numpy as jnp + + result_jax = matmul_dependent_on_backend(v, x) + return result_jax + + # torch backend + if keras.backend.backend() == "torch": + import torch + + result_torch = matmul_dependent_on_backend(v, x) + return result_torch + + # tensorflow backend + if keras.backend.backend() == "tensorflow": + import tensorflow as tf + + result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) + return result_tf ``` @@ -415,14 +546,10 @@ class VariableSelection(layers.Layer): def create_model(encoding_size): inputs = create_model_inputs() - feature_list = encode_inputs(inputs, encoding_size) - num_features = len(feature_list) - - features = VariableSelection(num_features, encoding_size, dropout_rate)( - feature_list - ) - + num_features = len(inputs) + features = VariableSelection(num_features, encoding_size, dropout_rate)(inputs) outputs = layers.Dense(units=1, activation="sigmoid")(features) + # Functional model model = keras.Model(inputs=inputs, outputs=outputs) return model @@ -436,7 +563,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 20 +num_epochs = 1 # maybe adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) @@ -445,10 +572,107 @@ model.compile( loss=keras.losses.BinaryCrossentropy(), metrics=[keras.metrics.BinaryAccuracy(name="accuracy")], ) +``` + +
+``` +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_1', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_2', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_3', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_4', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_5', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_6', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_7', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( + +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_8', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_9', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_10', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_11', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_12', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_13', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_14', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_15', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( + +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_16', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_17', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_18', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_19', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_20', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_21', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_22', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_23', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( + +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_24', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_25', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_26', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_27', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_28', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_29', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_30', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_31', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_32', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( + +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_33', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_34', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_35', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_36', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_37', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_38', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( +/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_39', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. + warnings.warn( + +``` +
+Let's visualize our connectivity graph: + + +```python +# `rankdir='LR'` is to make the graph horizontal. +keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, rankdir="LR") # Create an early stopping callback. -early_stopping = tf.keras.callbacks.EarlyStopping( +early_stopping = keras.callbacks.EarlyStopping( monitor="val_loss", patience=5, restore_best_weights=True ) @@ -473,52 +697,3090 @@ print(f"Test accuracy: {round(accuracy * 100, 2)}%")
``` - Start training the model... -Epoch 1/20 -640/640 [==============================] - 31s 29ms/step - loss: 253.8570 - accuracy: 0.9468 - val_loss: 229.4024 - val_accuracy: 0.9495 -Epoch 2/20 -640/640 [==============================] - 17s 25ms/step - loss: 229.9359 - accuracy: 0.9497 - val_loss: 223.4970 - val_accuracy: 0.9505 -Epoch 3/20 -640/640 [==============================] - 17s 25ms/step - loss: 225.5644 - accuracy: 0.9504 - val_loss: 222.0078 - val_accuracy: 0.9515 -Epoch 4/20 -640/640 [==============================] - 16s 25ms/step - loss: 222.2086 - accuracy: 0.9512 - val_loss: 218.2707 - val_accuracy: 0.9522 -Epoch 5/20 -640/640 [==============================] - 17s 25ms/step - loss: 218.0359 - accuracy: 0.9523 - val_loss: 217.3721 - val_accuracy: 0.9528 -Epoch 6/20 -640/640 [==============================] - 17s 26ms/step - loss: 214.8348 - accuracy: 0.9529 - val_loss: 210.3546 - val_accuracy: 0.9543 -Epoch 7/20 -640/640 [==============================] - 17s 26ms/step - loss: 213.0984 - accuracy: 0.9534 - val_loss: 210.2881 - val_accuracy: 0.9544 -Epoch 8/20 -640/640 [==============================] - 17s 26ms/step - loss: 211.6379 - accuracy: 0.9538 - val_loss: 209.3327 - val_accuracy: 0.9550 -Epoch 9/20 -640/640 [==============================] - 17s 26ms/step - loss: 210.7283 - accuracy: 0.9541 - val_loss: 209.5862 - val_accuracy: 0.9543 -Epoch 10/20 -640/640 [==============================] - 17s 26ms/step - loss: 209.9062 - accuracy: 0.9538 - val_loss: 210.1662 - val_accuracy: 0.9537 -Epoch 11/20 -640/640 [==============================] - 16s 25ms/step - loss: 209.6323 - accuracy: 0.9540 - val_loss: 207.9528 - val_accuracy: 0.9552 -Epoch 12/20 -640/640 [==============================] - 16s 25ms/step - loss: 208.7843 - accuracy: 0.9544 - val_loss: 207.5303 - val_accuracy: 0.9550 -Epoch 13/20 -640/640 [==============================] - 21s 32ms/step - loss: 207.9983 - accuracy: 0.9544 - val_loss: 206.8800 - val_accuracy: 0.9557 -Epoch 14/20 -640/640 [==============================] - 18s 28ms/step - loss: 207.2104 - accuracy: 0.9544 - val_loss: 216.0859 - val_accuracy: 0.9535 -Epoch 15/20 -640/640 [==============================] - 16s 25ms/step - loss: 207.2254 - accuracy: 0.9543 - val_loss: 206.7765 - val_accuracy: 0.9555 -Epoch 16/20 -640/640 [==============================] - 16s 25ms/step - loss: 206.6704 - accuracy: 0.9546 - val_loss: 206.7508 - val_accuracy: 0.9560 -Epoch 17/20 -640/640 [==============================] - 19s 30ms/step - loss: 206.1322 - accuracy: 0.9545 - val_loss: 205.9638 - val_accuracy: 0.9562 -Epoch 18/20 -640/640 [==============================] - 21s 31ms/step - loss: 205.4764 - accuracy: 0.9545 - val_loss: 206.0258 - val_accuracy: 0.9561 -Epoch 19/20 -640/640 [==============================] - 16s 25ms/step - loss: 204.3614 - accuracy: 0.9550 - val_loss: 207.1424 - val_accuracy: 0.9560 -Epoch 20/20 -640/640 [==============================] - 16s 25ms/step - loss: 203.9543 - accuracy: 0.9550 - val_loss: 206.4697 - val_accuracy: 0.9554 -Model training finished. -Evaluating model performance... -377/377 [==============================] - 4s 11ms/step - loss: 204.5099 - accuracy: 0.9547 -Test accuracy: 95.47% + +``` +
+ +
+``` + 1/Unknown 1s 639ms/step - accuracy: 0.7547 - loss: 1135.6592 + + + 2/Unknown 1s 213ms/step - accuracy: 0.7679 - loss: 1144.7844 + + + 3/Unknown 1s 212ms/step - accuracy: 0.7874 - loss: 1133.4303 + + + 4/Unknown 1s 217ms/step - accuracy: 0.8052 - loss: 1119.3180 + + + 5/Unknown 2s 218ms/step - accuracy: 0.8182 - loss: 1108.6187 + + + 6/Unknown 2s 219ms/step - accuracy: 0.8294 - loss: 1094.4551 + + + 7/Unknown 2s 219ms/step - accuracy: 0.8383 - loss: 1078.6390 + + + 8/Unknown 2s 216ms/step - accuracy: 0.8457 - loss: 1060.9896 + + + 9/Unknown 2s 218ms/step - accuracy: 0.8520 - loss: 1043.9601 + + + 10/Unknown 3s 219ms/step - accuracy: 0.8575 - loss: 1026.8148 + + + 11/Unknown 3s 220ms/step - accuracy: 0.8621 - loss: 1010.5887 + + + 12/Unknown 3s 220ms/step - accuracy: 0.8662 - loss: 994.9163 + + + 13/Unknown 3s 220ms/step - accuracy: 0.8698 - loss: 980.0228 + + + 14/Unknown 4s 221ms/step - accuracy: 0.8729 - loss: 965.6760 + + + 15/Unknown 4s 222ms/step - accuracy: 0.8756 - loss: 952.5342 + + + 16/Unknown 4s 222ms/step - 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Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset. + self._interrupted_warning() + + +``` +
+ 640/640 ━━━━━━━━━━━━━━━━━━━━ 224s 350ms/step - accuracy: 0.9348 - loss: 422.7237 - val_accuracy: 0.9369 - val_loss: 320.7682 + + +
+``` +Model training finished. +Evaluating model performance... + +``` +
+ +
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accuracy: 0.9375 - loss: 316.1200 + + +302/Unknown 72s 236ms/step - accuracy: 0.9375 - loss: 316.1111 + + +303/Unknown 72s 236ms/step - accuracy: 0.9376 - loss: 316.1031 + + +304/Unknown 72s 236ms/step - accuracy: 0.9376 - loss: 316.0949 + + +305/Unknown 72s 236ms/step - accuracy: 0.9376 - loss: 316.0871 + + +306/Unknown 73s 236ms/step - accuracy: 0.9376 - loss: 316.0789 + + +307/Unknown 73s 236ms/step - accuracy: 0.9376 - loss: 316.0715 + + +308/Unknown 73s 236ms/step - accuracy: 0.9376 - loss: 316.0636 + + +309/Unknown 73s 236ms/step - accuracy: 0.9376 - loss: 316.0563 + + +310/Unknown 73s 236ms/step - accuracy: 0.9376 - loss: 316.0502 + + +311/Unknown 74s 236ms/step - accuracy: 0.9376 - loss: 316.0448 + + +312/Unknown 74s 236ms/step - accuracy: 0.9376 - loss: 316.0383 + + +313/Unknown 74s 236ms/step - accuracy: 0.9376 - loss: 316.0314 + + +314/Unknown 74s 236ms/step - accuracy: 0.9376 - loss: 316.0244 + + +315/Unknown 75s 236ms/step - accuracy: 0.9376 - loss: 316.0172 + + +316/Unknown 75s 236ms/step - accuracy: 0.9376 - loss: 316.0094 + + +317/Unknown 75s 236ms/step - accuracy: 0.9376 - loss: 316.0017 + + +318/Unknown 75s 236ms/step - accuracy: 0.9376 - loss: 315.9943 + + +319/Unknown 76s 236ms/step - accuracy: 0.9376 - loss: 315.9870 + + +320/Unknown 76s 236ms/step - accuracy: 0.9376 - loss: 315.9799 + + +321/Unknown 76s 236ms/step - accuracy: 0.9376 - loss: 315.9724 + + +322/Unknown 76s 236ms/step - accuracy: 0.9376 - loss: 315.9654 + + +323/Unknown 76s 236ms/step - accuracy: 0.9376 - loss: 315.9579 + + +324/Unknown 77s 236ms/step - accuracy: 0.9376 - loss: 315.9504 + + +325/Unknown 77s 236ms/step - accuracy: 0.9377 - loss: 315.9435 + + +326/Unknown 77s 236ms/step - accuracy: 0.9377 - loss: 315.9373 + + +327/Unknown 77s 236ms/step - accuracy: 0.9377 - loss: 315.9312 + + +328/Unknown 77s 235ms/step - accuracy: 0.9377 - loss: 315.9245 + + +329/Unknown 78s 235ms/step - accuracy: 0.9377 - loss: 315.9179 + + +330/Unknown 78s 235ms/step - accuracy: 0.9377 - loss: 315.9118 + + +331/Unknown 78s 235ms/step - accuracy: 0.9377 - loss: 315.9054 + + +332/Unknown 78s 235ms/step - accuracy: 0.9377 - loss: 315.9005 + + +333/Unknown 79s 235ms/step - accuracy: 0.9377 - loss: 315.8957 + + +334/Unknown 79s 235ms/step - accuracy: 0.9377 - loss: 315.8907 + + +335/Unknown 79s 235ms/step - accuracy: 0.9377 - loss: 315.8854 + + +336/Unknown 79s 235ms/step - accuracy: 0.9377 - loss: 315.8794 + + +337/Unknown 80s 235ms/step - accuracy: 0.9377 - loss: 315.8741 + + +338/Unknown 80s 235ms/step - accuracy: 0.9377 - loss: 315.8690 + + +339/Unknown 80s 235ms/step - accuracy: 0.9377 - loss: 315.8629 + + +340/Unknown 80s 235ms/step - accuracy: 0.9377 - loss: 315.8569 + + +341/Unknown 80s 235ms/step - accuracy: 0.9377 - loss: 315.8515 + + +342/Unknown 81s 235ms/step - accuracy: 0.9377 - loss: 315.8463 + + +343/Unknown 81s 235ms/step - accuracy: 0.9377 - loss: 315.8414 + + +344/Unknown 81s 235ms/step - accuracy: 0.9377 - loss: 315.8365 + + +345/Unknown 81s 235ms/step - accuracy: 0.9377 - loss: 315.8317 + + +346/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8274 + + +347/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8230 + + +348/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8196 + + +349/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8168 + + +350/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8138 + + +351/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8109 + + +352/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8079 + + +353/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8058 + + +354/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8061 + + +355/Unknown 84s 235ms/step - accuracy: 0.9377 - loss: 315.8066 + + +356/Unknown 84s 235ms/step - accuracy: 0.9378 - loss: 315.8063 + + +357/Unknown 84s 235ms/step - accuracy: 0.9378 - loss: 315.8054 + + +358/Unknown 84s 235ms/step - accuracy: 0.9378 - loss: 315.8055 + + +359/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8056 + + +360/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8053 + + +361/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8054 + + +362/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8049 + + +363/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.8039 + + +364/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.8022 + + +365/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.8002 + + +366/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.7970 + + +367/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7933 + + +368/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7896 + + +369/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7862 + + +370/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7821 + + +371/Unknown 88s 235ms/step - accuracy: 0.9378 - loss: 315.7777 + + +372/Unknown 88s 235ms/step - accuracy: 0.9378 - loss: 315.7731 + + +373/Unknown 88s 236ms/step - accuracy: 0.9378 - loss: 315.7687 + + +374/Unknown 88s 236ms/step - accuracy: 0.9378 - loss: 315.7640 + + +375/Unknown 89s 236ms/step - accuracy: 0.9378 - loss: 315.7592 + + +376/Unknown 89s 236ms/step - accuracy: 0.9378 - loss: 315.7550 + + +377/Unknown 89s 236ms/step - accuracy: 0.9378 - loss: 315.7512 + + +``` +
+ 377/377 ━━━━━━━━━━━━━━━━━━━━ 89s 236ms/step - accuracy: 0.9378 - loss: 315.7473 + + +
+``` +Test accuracy: 93.87% ```
From 0da607c902e146eeab43f9150fa5d0663963208d Mon Sep 17 00:00:00 2001 From: Humbulani Date: Wed, 8 Jan 2025 14:56:05 +0200 Subject: [PATCH 2/6] adapting the script classification_with_grn_and_vsn to be Backend-Agnostic --- .../classification_with_grn_and_vsn.py | 49 +- .../classification_with_grn_and_vsn.ipynb | 49 +- .../md/classification_with_grn_and_vsn.md | 2314 +++++++++-------- 3 files changed, 1213 insertions(+), 1199 deletions(-) diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index 809f07fa23..9964597bae 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -399,6 +399,21 @@ class VariableSelection(layers.Layer): def __init__(self, num_features, units, dropout_rate): super().__init__() self.units = units + # Create an embedding layers with the specified dimensions + self.embeddings = dict() + for input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY: + vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_] + embedding_encoder = layers.Embedding( + input_dim=len(vocabulary), output_dim=self.units, name=input_ + ) + self.embeddings[input_] = embedding_encoder + + # Projection layers for numeric features + self.proj_layer = dict() + for input_ in NUMERIC_FEATURE_NAMES: + proj_layer = layers.Dense(units=self.units) + self.proj_layer[input_] = proj_layer + self.grns = list() # Create a GRN for each feature independently for idx in range(num_features): @@ -412,21 +427,16 @@ def call(self, inputs): concat_inputs = [] for input_ in inputs: if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY: - vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_] - # Create an embedding layer with the specified dimensions - embedding_encoder = layers.Embedding( - input_dim=len(vocabulary), output_dim=self.units - ) - max_index = len(vocabulary) - 1 # Clamp the indices + max_index = self.embeddings[input_].input_dim - 1 # Clamp the indices # torch had some index errors during embedding hence the clip function - embedded_feature = embedding_encoder( + embedded_feature = self.embeddings[input_]( keras.ops.clip(inputs[input_], 0, max_index) ) concat_inputs.append(embedded_feature) else: # Project the numeric feature to encoding_size using linear transformation. proj_feature = keras.ops.expand_dims(inputs[input_], -1) - proj_feature = layers.Dense(units=self.units)(proj_feature) + proj_feature = self.proj_layer[input_](proj_feature) concat_inputs.append(proj_feature) v = layers.concatenate(concat_inputs) @@ -438,11 +448,10 @@ def call(self, inputs): x = keras.ops.stack(x, axis=1) # The reason for each individual backend calculation is that I couldn't find - # the equivalent keras operation that is backend-agnostic. In the following case there's + # the equivalent keras operation that is backend-agnostic. In the following case there,s # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul # for all backends, but due to jax jit tracing it results in an error. - - def matmul_dependent_on_backend(tensor_1, tensor_2): + def matmul_dependent_on_backend(thsi, v): """ Function for executing matmul for each backend. """ @@ -450,18 +459,12 @@ def matmul_dependent_on_backend(tensor_1, tensor_2): if keras.backend.backend() == "jax": import jax.numpy as jnp - result = jnp.sum(tensor_1 * tensor_2, axis=1) + result = jnp.sum(thsi * v, axis=1) elif keras.backend.backend() == "torch": - import torch - - result = torch.sum(tensor_1 * tensor_2, dim=1) + result = torch.sum(thsi * v, dim=1) # tensorflow backend elif keras.backend.backend() == "tensorflow": - import tensorflow as tf - - result = keras.ops.squeeze( - tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1 - ) + result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1) # unsupported backend exception else: raise ValueError( @@ -477,14 +480,12 @@ def matmul_dependent_on_backend(tensor_1, tensor_2): result_jax = matmul_dependent_on_backend(v, x) return result_jax - # torch backend if keras.backend.backend() == "torch": import torch result_torch = matmul_dependent_on_backend(v, x) return result_torch - # tensorflow backend if keras.backend.backend() == "tensorflow": import tensorflow as tf @@ -492,6 +493,10 @@ def matmul_dependent_on_backend(tensor_1, tensor_2): result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) return result_tf + # to remove the build warnings + def build(self): + self.built = True + """ ## Create Gated Residual and Variable Selection Networks model diff --git a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb index 147943e8b1..1180954978 100644 --- a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb +++ b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb @@ -595,6 +595,21 @@ " def __init__(self, num_features, units, dropout_rate):\n", " super().__init__()\n", " self.units = units\n", + " # Create an embedding layers with the specified dimensions\n", + " self.embeddings = dict()\n", + " for input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n", + " vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_]\n", + " embedding_encoder = layers.Embedding(\n", + " input_dim=len(vocabulary), output_dim=self.units, name=input_\n", + " )\n", + " self.embeddings[input_] = embedding_encoder\n", + "\n", + " # Projection layers for numeric features\n", + " self.proj_layer = dict()\n", + " for input_ in NUMERIC_FEATURE_NAMES:\n", + " proj_layer = layers.Dense(units=self.units)\n", + " self.proj_layer[input_] = proj_layer\n", + "\n", " self.grns = list()\n", " # Create a GRN for each feature independently\n", " for idx in range(num_features):\n", @@ -608,21 +623,16 @@ " concat_inputs = []\n", " for input_ in inputs:\n", " if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY:\n", - " vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_]\n", - " # Create an embedding layer with the specified dimensions\n", - " embedding_encoder = layers.Embedding(\n", - " input_dim=len(vocabulary), output_dim=self.units\n", - " )\n", - " max_index = len(vocabulary) - 1 # Clamp the indices\n", + " max_index = self.embeddings[input_].input_dim - 1 # Clamp the indices\n", " # torch had some index errors during embedding hence the clip function\n", - " embedded_feature = embedding_encoder(\n", + " embedded_feature = self.embeddings[input_](\n", " keras.ops.clip(inputs[input_], 0, max_index)\n", " )\n", " concat_inputs.append(embedded_feature)\n", " else:\n", " # Project the numeric feature to encoding_size using linear transformation.\n", " proj_feature = keras.ops.expand_dims(inputs[input_], -1)\n", - " proj_feature = layers.Dense(units=self.units)(proj_feature)\n", + " proj_feature = self.proj_layer[input_](proj_feature)\n", " concat_inputs.append(proj_feature)\n", "\n", " v = layers.concatenate(concat_inputs)\n", @@ -634,11 +644,10 @@ " x = keras.ops.stack(x, axis=1)\n", "\n", " # The reason for each individual backend calculation is that I couldn't find\n", - " # the equivalent keras operation that is backend-agnostic. In the following case there's\n", + " # the equivalent keras operation that is backend-agnostic. In the following case there,s\n", " # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul\n", " # for all backends, but due to jax jit tracing it results in an error.\n", - "\n", - " def matmul_dependent_on_backend(tensor_1, tensor_2):\n", + " def matmul_dependent_on_backend(thsi, v):\n", " \"\"\"\n", " Function for executing matmul for each backend.\n", " \"\"\"\n", @@ -646,18 +655,12 @@ " if keras.backend.backend() == \"jax\":\n", " import jax.numpy as jnp\n", "\n", - " result = jnp.sum(tensor_1 * tensor_2, axis=1)\n", + " result = jnp.sum(thsi * v, axis=1)\n", " elif keras.backend.backend() == \"torch\":\n", - " import torch\n", - "\n", - " result = torch.sum(tensor_1 * tensor_2, dim=1)\n", + " result = torch.sum(thsi * v, dim=1)\n", " # tensorflow backend\n", " elif keras.backend.backend() == \"tensorflow\":\n", - " import tensorflow as tf\n", - "\n", - " result = keras.ops.squeeze(\n", - " tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1\n", - " )\n", + " result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1)\n", " # unsupported backend exception\n", " else:\n", " raise ValueError(\n", @@ -673,20 +676,22 @@ "\n", " result_jax = matmul_dependent_on_backend(v, x)\n", " return result_jax\n", - "\n", " # torch backend\n", " if keras.backend.backend() == \"torch\":\n", " import torch\n", "\n", " result_torch = matmul_dependent_on_backend(v, x)\n", " return result_torch\n", - "\n", " # tensorflow backend\n", " if keras.backend.backend() == \"tensorflow\":\n", " import tensorflow as tf\n", "\n", " result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)\n", " return result_tf\n", + "\n", + " # to remove the build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, diff --git a/examples/structured_data/md/classification_with_grn_and_vsn.md b/examples/structured_data/md/classification_with_grn_and_vsn.md index 5f7ed58c67..602be61966 100644 --- a/examples/structured_data/md/classification_with_grn_and_vsn.md +++ b/examples/structured_data/md/classification_with_grn_and_vsn.md @@ -443,6 +443,21 @@ class VariableSelection(layers.Layer): def __init__(self, num_features, units, dropout_rate): super().__init__() self.units = units + # Create an embedding layers with the specified dimensions + self.embeddings = dict() + for input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY: + vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_] + embedding_encoder = layers.Embedding( + input_dim=len(vocabulary), output_dim=self.units, name=input_ + ) + self.embeddings[input_] = embedding_encoder + + # Projection layers for numeric features + self.proj_layer = dict() + for input_ in NUMERIC_FEATURE_NAMES: + proj_layer = layers.Dense(units=self.units) + self.proj_layer[input_] = proj_layer + self.grns = list() # Create a GRN for each feature independently for idx in range(num_features): @@ -456,21 +471,16 @@ class VariableSelection(layers.Layer): concat_inputs = [] for input_ in inputs: if input_ in CATEGORICAL_FEATURES_WITH_VOCABULARY: - vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[input_] - # Create an embedding layer with the specified dimensions - embedding_encoder = layers.Embedding( - input_dim=len(vocabulary), output_dim=self.units - ) - max_index = len(vocabulary) - 1 # Clamp the indices + max_index = self.embeddings[input_].input_dim - 1 # Clamp the indices # torch had some index errors during embedding hence the clip function - embedded_feature = embedding_encoder( + embedded_feature = self.embeddings[input_]( keras.ops.clip(inputs[input_], 0, max_index) ) concat_inputs.append(embedded_feature) else: # Project the numeric feature to encoding_size using linear transformation. proj_feature = keras.ops.expand_dims(inputs[input_], -1) - proj_feature = layers.Dense(units=self.units)(proj_feature) + proj_feature = self.proj_layer[input_](proj_feature) concat_inputs.append(proj_feature) v = layers.concatenate(concat_inputs) @@ -482,11 +492,10 @@ class VariableSelection(layers.Layer): x = keras.ops.stack(x, axis=1) # The reason for each individual backend calculation is that I couldn't find - # the equivalent keras operation that is backend-agnostic. In the following case there's + # the equivalent keras operation that is backend-agnostic. In the following case there,s # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul # for all backends, but due to jax jit tracing it results in an error. - - def matmul_dependent_on_backend(tensor_1, tensor_2): + def matmul_dependent_on_backend(thsi, v): """ Function for executing matmul for each backend. """ @@ -494,18 +503,12 @@ class VariableSelection(layers.Layer): if keras.backend.backend() == "jax": import jax.numpy as jnp - result = jnp.sum(tensor_1 * tensor_2, axis=1) + result = jnp.sum(thsi * v, axis=1) elif keras.backend.backend() == "torch": - import torch - - result = torch.sum(tensor_1 * tensor_2, dim=1) + result = torch.sum(thsi * v, dim=1) # tensorflow backend elif keras.backend.backend() == "tensorflow": - import tensorflow as tf - - result = keras.ops.squeeze( - tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1 - ) + result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1) # unsupported backend exception else: raise ValueError( @@ -521,14 +524,12 @@ class VariableSelection(layers.Layer): result_jax = matmul_dependent_on_backend(v, x) return result_jax - # torch backend if keras.backend.backend() == "torch": import torch result_torch = matmul_dependent_on_backend(v, x) return result_torch - # tensorflow backend if keras.backend.backend() == "tensorflow": import tensorflow as tf @@ -536,6 +537,10 @@ class VariableSelection(layers.Layer): result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) return result_tf + # to remove the build warnings + def build(self): + self.built = True + ``` --- @@ -592,13 +597,13 @@ model.compile( warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_7', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( - /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_8', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_9', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_10', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( + /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_11', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_12', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. @@ -609,7 +614,6 @@ model.compile( warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_15', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( - /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_16', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_17', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. @@ -620,13 +624,13 @@ model.compile( warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_20', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( + /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_21', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_22', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_23', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( - /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_24', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_25', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. @@ -643,9 +647,9 @@ model.compile( warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_31', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( + /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_32', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( - /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_33', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_34', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. @@ -704,1924 +708,1924 @@ Start training the model...
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loss: 982.8596 - - 9/Unknown 2s 218ms/step - accuracy: 0.8520 - loss: 1043.9601 + + 9/Unknown 2s 215ms/step - accuracy: 0.7121 - loss: 953.8212 - - 10/Unknown 3s 219ms/step - accuracy: 0.8575 - loss: 1026.8148 + + 10/Unknown 3s 214ms/step - accuracy: 0.7272 - loss: 927.4525 - - 11/Unknown 3s 220ms/step - accuracy: 0.8621 - loss: 1010.5887 + + 11/Unknown 3s 214ms/step - accuracy: 0.7400 - loss: 904.0518 - - 12/Unknown 3s 220ms/step - accuracy: 0.8662 - loss: 994.9163 + + 12/Unknown 3s 215ms/step - accuracy: 0.7512 - loss: 882.7589  - 13/Unknown 3s 220ms/step - accuracy: 0.8698 - loss: 980.0228 + 13/Unknown 3s 219ms/step - accuracy: 0.7611 - loss: 862.8194  - 14/Unknown 4s 221ms/step - accuracy: 0.8729 - loss: 965.6760 + 14/Unknown 4s 219ms/step - accuracy: 0.7700 - loss: 844.1758  - 15/Unknown 4s 222ms/step - accuracy: 0.8756 - loss: 952.5342 + 15/Unknown 4s 222ms/step - accuracy: 0.7780 - loss: 826.9224  - 16/Unknown 4s 222ms/step - accuracy: 0.8780 - loss: 939.8143 + 16/Unknown 4s 224ms/step - 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accuracy: 0.9348 - loss: 423.0203 +638/Unknown 157s 245ms/step - accuracy: 0.9357 - loss: 310.3852  -639/Unknown 198s 309ms/step - accuracy: 0.9348 - loss: 422.9211 +639/Unknown 157s 245ms/step - accuracy: 0.9357 - loss: 310.3038  -640/Unknown 198s 309ms/step - accuracy: 0.9348 - loss: 422.8223 +640/Unknown 157s 245ms/step - accuracy: 0.9357 - loss: 310.2225 /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset. self._interrupted_warning() @@ -2629,7 +2633,7 @@ Start training the model...  ```
- 640/640 ━━━━━━━━━━━━━━━━━━━━ 224s 350ms/step - accuracy: 0.9348 - loss: 422.7237 - val_accuracy: 0.9369 - val_loss: 320.7682 + 640/640 ━━━━━━━━━━━━━━━━━━━━ 174s 271ms/step - accuracy: 0.9358 - loss: 310.1415 - val_accuracy: 0.9498 - val_loss: 227.1810
@@ -2642,1145 +2646,1145 @@ Evaluating model performance...
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9/Unknown 2s 227ms/step - accuracy: 0.9347 - loss: 295.5774 + 9/Unknown 2s 145ms/step - accuracy: 0.9441 - loss: 214.7674  - 10/Unknown 3s 230ms/step - accuracy: 0.9350 - loss: 296.0671 + 10/Unknown 2s 144ms/step - accuracy: 0.9446 - loss: 213.8222  - 11/Unknown 3s 228ms/step - accuracy: 0.9352 - loss: 296.4334 + 11/Unknown 2s 144ms/step - accuracy: 0.9452 - loss: 213.1922  - 12/Unknown 3s 228ms/step - accuracy: 0.9355 - loss: 296.5976 + 12/Unknown 2s 144ms/step - accuracy: 0.9455 - loss: 212.5974  - 13/Unknown 3s 229ms/step - accuracy: 0.9358 - loss: 296.8359 + 13/Unknown 2s 144ms/step - accuracy: 0.9457 - loss: 212.4554  - 14/Unknown 3s 228ms/step - accuracy: 0.9360 - loss: 296.8992 + 14/Unknown 2s 144ms/step - accuracy: 0.9459 - loss: 212.1609  - 15/Unknown 4s 228ms/step - accuracy: 0.9363 - loss: 297.1031 + 15/Unknown 2s 145ms/step - accuracy: 0.9461 - loss: 212.0100  - 16/Unknown 4s 228ms/step - accuracy: 0.9365 - loss: 297.1199 + 16/Unknown 3s 145ms/step - accuracy: 0.9464 - loss: 211.6899  - 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accuracy: 0.9489 - loss: 227.1095  -344/Unknown 81s 235ms/step - accuracy: 0.9377 - loss: 315.8365 +344/Unknown 52s 150ms/step - accuracy: 0.9489 - loss: 227.1141  -345/Unknown 81s 235ms/step - accuracy: 0.9377 - loss: 315.8317 +345/Unknown 52s 150ms/step - accuracy: 0.9489 - loss: 227.1185  -346/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8274 +346/Unknown 52s 150ms/step - accuracy: 0.9489 - loss: 227.1235  -347/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8230 +347/Unknown 52s 150ms/step - accuracy: 0.9489 - loss: 227.1284  -348/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8196 +348/Unknown 52s 150ms/step - accuracy: 0.9489 - loss: 227.1345  -349/Unknown 82s 235ms/step - accuracy: 0.9377 - loss: 315.8168 +349/Unknown 53s 150ms/step - accuracy: 0.9489 - loss: 227.1408  -350/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8138 +350/Unknown 53s 150ms/step - accuracy: 0.9489 - loss: 227.1469  -351/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8109 +351/Unknown 53s 150ms/step - accuracy: 0.9489 - loss: 227.1535  -352/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8079 +352/Unknown 53s 150ms/step - accuracy: 0.9490 - loss: 227.1598  -353/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8058 +353/Unknown 53s 150ms/step - accuracy: 0.9490 - loss: 227.1664  -354/Unknown 83s 235ms/step - accuracy: 0.9377 - loss: 315.8061 +354/Unknown 53s 150ms/step - accuracy: 0.9490 - loss: 227.1744  -355/Unknown 84s 235ms/step - accuracy: 0.9377 - loss: 315.8066 +355/Unknown 53s 150ms/step - accuracy: 0.9490 - loss: 227.1824  -356/Unknown 84s 235ms/step - accuracy: 0.9378 - loss: 315.8063 +356/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.1902  -357/Unknown 84s 235ms/step - accuracy: 0.9378 - loss: 315.8054 +357/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.1976  -358/Unknown 84s 235ms/step - accuracy: 0.9378 - loss: 315.8055 +358/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2054  -359/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8056 +359/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2136  -360/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8053 +360/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2215  -361/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8054 +361/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2299  -362/Unknown 85s 235ms/step - accuracy: 0.9378 - loss: 315.8049 +362/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2383  -363/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.8039 +363/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2464  -364/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.8022 +364/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2545  -365/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.8002 +365/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2623  -366/Unknown 86s 235ms/step - accuracy: 0.9378 - loss: 315.7970 +366/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2691  -367/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7933 +367/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2757  -368/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7896 +368/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2823  -369/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7862 +369/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2889  -370/Unknown 87s 235ms/step - accuracy: 0.9378 - loss: 315.7821 +370/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2951  -371/Unknown 88s 235ms/step - accuracy: 0.9378 - loss: 315.7777 +371/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3009  -372/Unknown 88s 235ms/step - accuracy: 0.9378 - loss: 315.7731 +372/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3066  -373/Unknown 88s 236ms/step - accuracy: 0.9378 - loss: 315.7687 +373/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3122  -374/Unknown 88s 236ms/step - accuracy: 0.9378 - loss: 315.7640 +374/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3181  -375/Unknown 89s 236ms/step - accuracy: 0.9378 - loss: 315.7592 +375/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3240  -376/Unknown 89s 236ms/step - accuracy: 0.9378 - loss: 315.7550 +376/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3297  -377/Unknown 89s 236ms/step - accuracy: 0.9378 - loss: 315.7512 +377/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3355  ```
- 377/377 ━━━━━━━━━━━━━━━━━━━━ 89s 236ms/step - accuracy: 0.9378 - loss: 315.7473 + 377/377 ━━━━━━━━━━━━━━━━━━━━ 56s 149ms/step - accuracy: 0.9490 - loss: 227.3412
``` -Test accuracy: 93.87% +Test accuracy: 95.0% ```
From 6399fe26c3bbba848cabd005b01acc27958ce2bb Mon Sep 17 00:00:00 2001 From: Humbulani Date: Wed, 8 Jan 2025 16:11:11 +0200 Subject: [PATCH 3/6] script variable name changes refactoring --- .../structured_data/classification_with_grn_and_vsn.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index 9964597bae..49164a600a 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -451,7 +451,7 @@ def call(self, inputs): # the equivalent keras operation that is backend-agnostic. In the following case there,s # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul # for all backends, but due to jax jit tracing it results in an error. - def matmul_dependent_on_backend(thsi, v): + def matmul_dependent_on_backend(tensor_1, tensor_2): """ Function for executing matmul for each backend. """ @@ -459,12 +459,12 @@ def matmul_dependent_on_backend(thsi, v): if keras.backend.backend() == "jax": import jax.numpy as jnp - result = jnp.sum(thsi * v, axis=1) + result = jnp.sum(tensor_1 * tensor_2, axis=1) elif keras.backend.backend() == "torch": - result = torch.sum(thsi * v, dim=1) + result = torch.sum(tensor_1 * tensor_2, dim=1) # tensorflow backend elif keras.backend.backend() == "tensorflow": - result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1) + result = keras.ops.squeeze(tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1) # unsupported backend exception else: raise ValueError( From 2aaadce031d3d40b7764edd3643b716770d0faa2 Mon Sep 17 00:00:00 2001 From: Humbulani Date: Tue, 14 Jan 2025 00:30:35 +0200 Subject: [PATCH 4/6] addressing the PR comments for the script: classification_with_grn_and_vsn.py --- .../classification_with_grn_and_vsn.py | 73 +- .../classification_with_grn_and_vsn.ipynb | 87 +- .../md/classification_with_grn_and_vsn.md | 2314 ++++++++--------- 3 files changed, 1106 insertions(+), 1368 deletions(-) diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index 49164a600a..39611755aa 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -182,20 +182,6 @@ valid_data.to_csv(valid_data_file, index=False, header=False) test_data.to_csv(test_data_file, index=False, header=False) -""" -Clean the directory for the downloaded files except the .tar.gz file and -also remove the empty directories -""" - -subprocess.run( - f'find {extracted_path} -type f ! -name "*.tar.gz" -exec rm -f {{}} +', - shell=True, - check=True, -) -subprocess.run( - f"find {extracted_path} -type d -empty -exec rmdir {{}} +", shell=True, check=True -) - """ ## Define dataset metadata @@ -337,6 +323,10 @@ def __init__(self, units): def call(self, inputs): return self.linear(inputs) * self.sigmoid(inputs) + # Remove build warnings + def build(self): + self.built = True + """ ## Implement the Gated Residual Network @@ -372,6 +362,10 @@ def call(self, inputs): x = self.layer_norm(x) return x + # Remove build warnings + def build(self): + self.built = True + """ ## Implement the Variable Selection Network @@ -446,52 +440,9 @@ def call(self, inputs): for idx, input in enumerate(concat_inputs): x.append(self.grns[idx](input)) x = keras.ops.stack(x, axis=1) - - # The reason for each individual backend calculation is that I couldn't find - # the equivalent keras operation that is backend-agnostic. In the following case there,s - # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul - # for all backends, but due to jax jit tracing it results in an error. - def matmul_dependent_on_backend(tensor_1, tensor_2): - """ - Function for executing matmul for each backend. - """ - # jax backend - if keras.backend.backend() == "jax": - import jax.numpy as jnp - - result = jnp.sum(tensor_1 * tensor_2, axis=1) - elif keras.backend.backend() == "torch": - result = torch.sum(tensor_1 * tensor_2, dim=1) - # tensorflow backend - elif keras.backend.backend() == "tensorflow": - result = keras.ops.squeeze(tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1) - # unsupported backend exception - else: - raise ValueError( - "Unsupported backend: {}".format(keras.backend.backend()) - ) - return result - - # jax backend - if keras.backend.backend() == "jax": - # This repetative imports are intentional to force the idea of backend - # separation - import jax.numpy as jnp - - result_jax = matmul_dependent_on_backend(v, x) - return result_jax - # torch backend - if keras.backend.backend() == "torch": - import torch - - result_torch = matmul_dependent_on_backend(v, x) - return result_torch - # tensorflow backend - if keras.backend.backend() == "tensorflow": - import tensorflow as tf - - result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) - return result_tf + return keras.ops.squeeze( + keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1 + ) # to remove the build warnings def build(self): @@ -520,7 +471,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 1 # maybe adjusted to a desired value +num_epochs = 1 # may be adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) diff --git a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb index 1180954978..40aa463bb8 100644 --- a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb +++ b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb @@ -282,34 +282,6 @@ "test_data.to_csv(test_data_file, index=False, header=False)" ] }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text" - }, - "source": [ - "Clean the directory for the downloaded files except the .tar.gz file and\n", - "also remove the empty directories" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab_type": "code" - }, - "outputs": [], - "source": [ - "subprocess.run(\n", - " f'find {extracted_path} -type f ! -name \"*.tar.gz\" -exec rm -f {{}} +',\n", - " shell=True,\n", - " check=True,\n", - ")\n", - "subprocess.run(\n", - " f\"find {extracted_path} -type d -empty -exec rmdir {{}} +\", shell=True, check=True\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": { @@ -505,6 +477,10 @@ "\n", " def call(self, inputs):\n", " return self.linear(inputs) * self.sigmoid(inputs)\n", + "\n", + " # Remove build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, @@ -554,6 +530,10 @@ " x = inputs + self.gated_linear_unit(x)\n", " x = self.layer_norm(x)\n", " return x\n", + "\n", + " # Remove build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, @@ -642,52 +622,9 @@ " for idx, input in enumerate(concat_inputs):\n", " x.append(self.grns[idx](input))\n", " x = keras.ops.stack(x, axis=1)\n", - "\n", - " # The reason for each individual backend calculation is that I couldn't find\n", - " # the equivalent keras operation that is backend-agnostic. In the following case there,s\n", - " # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul\n", - " # for all backends, but due to jax jit tracing it results in an error.\n", - " def matmul_dependent_on_backend(thsi, v):\n", - " \"\"\"\n", - " Function for executing matmul for each backend.\n", - " \"\"\"\n", - " # jax backend\n", - " if keras.backend.backend() == \"jax\":\n", - " import jax.numpy as jnp\n", - "\n", - " result = jnp.sum(thsi * v, axis=1)\n", - " elif keras.backend.backend() == \"torch\":\n", - " result = torch.sum(thsi * v, dim=1)\n", - " # tensorflow backend\n", - " elif keras.backend.backend() == \"tensorflow\":\n", - " result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1)\n", - " # unsupported backend exception\n", - " else:\n", - " raise ValueError(\n", - " \"Unsupported backend: {}\".format(keras.backend.backend())\n", - " )\n", - " return result\n", - "\n", - " # jax backend\n", - " if keras.backend.backend() == \"jax\":\n", - " # This repetative imports are intentional to force the idea of backend\n", - " # separation\n", - " import jax.numpy as jnp\n", - "\n", - " result_jax = matmul_dependent_on_backend(v, x)\n", - " return result_jax\n", - " # torch backend\n", - " if keras.backend.backend() == \"torch\":\n", - " import torch\n", - "\n", - " result_torch = matmul_dependent_on_backend(v, x)\n", - " return result_torch\n", - " # tensorflow backend\n", - " if keras.backend.backend() == \"tensorflow\":\n", - " import tensorflow as tf\n", - "\n", - " result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)\n", - " return result_tf\n", + " return keras.ops.squeeze(\n", + " keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1\n", + " )\n", "\n", " # to remove the build warnings\n", " def build(self):\n", @@ -744,7 +681,7 @@ "learning_rate = 0.001\n", "dropout_rate = 0.15\n", "batch_size = 265\n", - "num_epochs = 1 # maybe adjusted to a desired value\n", + "num_epochs = 1 # may be adjusted to a desired value\n", "encoding_size = 16\n", "\n", "model = create_model(encoding_size)\n", diff --git a/examples/structured_data/md/classification_with_grn_and_vsn.md b/examples/structured_data/md/classification_with_grn_and_vsn.md index 602be61966..6bd5bd9539 100644 --- a/examples/structured_data/md/classification_with_grn_and_vsn.md +++ b/examples/structured_data/md/classification_with_grn_and_vsn.md @@ -206,30 +206,6 @@ valid_data.to_csv(valid_data_file, index=False, header=False) test_data.to_csv(test_data_file, index=False, header=False) ``` -Clean the directory for the downloaded files except the .tar.gz file and -also remove the empty directories - - -```python -subprocess.run( - f'find {extracted_path} -type f ! -name "*.tar.gz" -exec rm -f {{}} +', - shell=True, - check=True, -) -subprocess.run( - f"find {extracted_path} -type d -empty -exec rmdir {{}} +", shell=True, check=True -) -``` - - - - -
-``` -CompletedProcess(args='find /home/humbulani/.keras/datasets/census+income+kdd.zip -type d -empty -exec rmdir {} +', returncode=0) - -``` -
--- ## Define dataset metadata @@ -378,6 +354,10 @@ class GatedLinearUnit(layers.Layer): def call(self, inputs): return self.linear(inputs) * self.sigmoid(inputs) + # Remove build warnings + def build(self): + self.built = True + ``` --- @@ -415,6 +395,10 @@ class GatedResidualNetwork(layers.Layer): x = self.layer_norm(x) return x + # Remove build warnings + def build(self): + self.built = True + ``` --- @@ -490,52 +474,9 @@ class VariableSelection(layers.Layer): for idx, input in enumerate(concat_inputs): x.append(self.grns[idx](input)) x = keras.ops.stack(x, axis=1) - - # The reason for each individual backend calculation is that I couldn't find - # the equivalent keras operation that is backend-agnostic. In the following case there,s - # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul - # for all backends, but due to jax jit tracing it results in an error. - def matmul_dependent_on_backend(thsi, v): - """ - Function for executing matmul for each backend. - """ - # jax backend - if keras.backend.backend() == "jax": - import jax.numpy as jnp - - result = jnp.sum(thsi * v, axis=1) - elif keras.backend.backend() == "torch": - result = torch.sum(thsi * v, dim=1) - # tensorflow backend - elif keras.backend.backend() == "tensorflow": - result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1) - # unsupported backend exception - else: - raise ValueError( - "Unsupported backend: {}".format(keras.backend.backend()) - ) - return result - - # jax backend - if keras.backend.backend() == "jax": - # This repetative imports are intentional to force the idea of backend - # separation - import jax.numpy as jnp - - result_jax = matmul_dependent_on_backend(v, x) - return result_jax - # torch backend - if keras.backend.backend() == "torch": - import torch - - result_torch = matmul_dependent_on_backend(v, x) - return result_torch - # tensorflow backend - if keras.backend.backend() == "tensorflow": - import tensorflow as tf - - result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) - return result_tf + return keras.ops.squeeze( + keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1 + ) # to remove the build warnings def build(self): @@ -568,7 +509,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 1 # maybe adjusted to a desired value +num_epochs = 1 # may be adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) @@ -579,94 +520,6 @@ model.compile( ) ``` -
-``` -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_1', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_2', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_3', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_4', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_5', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_6', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_7', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_8', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_9', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_10', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( - -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_11', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_12', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_13', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_14', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_15', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_16', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_17', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_18', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_19', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_20', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( - -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_21', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_22', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_23', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_24', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_25', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_26', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_27', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_28', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_29', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_30', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_31', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( - -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_32', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_33', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_34', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_35', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_36', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_37', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_38', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_39', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method. - warnings.warn( - -``` -
Let's visualize our connectivity graph: @@ -708,1924 +561,1921 @@ Start training the model...
``` - 1/Unknown 1s 740ms/step - accuracy: 0.2491 - loss: 1302.2852 + 1/Unknown 1s 698ms/step - accuracy: 0.4717 - loss: 1212.3043  - 2/Unknown 1s 193ms/step - accuracy: 0.4104 - loss: 1243.1028 + 2/Unknown 1s 200ms/step - accuracy: 0.5745 - loss: 1141.6052  - 3/Unknown 1s 207ms/step - accuracy: 0.5046 - loss: 1190.0552 + 3/Unknown 1s 195ms/step - accuracy: 0.6388 - loss: 1084.4358  - 4/Unknown 1s 211ms/step - accuracy: 0.5667 - loss: 1140.0157 + 4/Unknown 1s 199ms/step - accuracy: 0.6822 - loss: 1031.0354  - 5/Unknown 2s 211ms/step - accuracy: 0.6118 - loss: 1094.2517 + 5/Unknown 2s 201ms/step - accuracy: 0.7131 - loss: 986.4984 - - 6/Unknown 2s 215ms/step - accuracy: 0.6458 - loss: 1052.5693 + + 6/Unknown 2s 197ms/step - accuracy: 0.7363 - loss: 947.2644 - - 7/Unknown 2s 216ms/step - accuracy: 0.6727 - loss: 1015.1872 + + 7/Unknown 2s 190ms/step - accuracy: 0.7546 - loss: 912.4213 - - 8/Unknown 2s 215ms/step - accuracy: 0.6942 - loss: 982.8596 + + 8/Unknown 2s 188ms/step - accuracy: 0.7698 - loss: 881.4526  - 9/Unknown 2s 215ms/step - accuracy: 0.7121 - loss: 953.8212 + 9/Unknown 2s 186ms/step - accuracy: 0.7824 - loss: 853.8523  - 10/Unknown 3s 214ms/step - accuracy: 0.7272 - loss: 927.4525 + 10/Unknown 2s 184ms/step - accuracy: 0.7932 - loss: 829.0496  - 11/Unknown 3s 214ms/step - accuracy: 0.7400 - loss: 904.0518 + 11/Unknown 3s 183ms/step - accuracy: 0.8022 - loss: 807.4752  - 12/Unknown 3s 215ms/step - accuracy: 0.7512 - loss: 882.7589 + 12/Unknown 3s 184ms/step - accuracy: 0.8100 - loss: 788.1222  - 13/Unknown 3s 219ms/step - accuracy: 0.7611 - loss: 862.8194 + 13/Unknown 3s 187ms/step - accuracy: 0.8170 - loss: 770.3723  - 14/Unknown 4s 219ms/step - accuracy: 0.7700 - loss: 844.1758 + 14/Unknown 3s 187ms/step - accuracy: 0.8233 - loss: 753.6734  - 15/Unknown 4s 222ms/step - accuracy: 0.7780 - loss: 826.9224 + 15/Unknown 3s 186ms/step - accuracy: 0.8289 - loss: 737.9523  - 16/Unknown 4s 224ms/step - accuracy: 0.7851 - loss: 810.8615 + 16/Unknown 3s 186ms/step - accuracy: 0.8342 - loss: 723.0760  - 17/Unknown 4s 224ms/step - accuracy: 0.7916 - loss: 795.8868 + 17/Unknown 4s 186ms/step - accuracy: 0.8389 - loss: 709.2202  - 18/Unknown 5s 223ms/step - accuracy: 0.7975 - loss: 782.1332 + 18/Unknown 4s 202ms/step - accuracy: 0.8432 - loss: 696.8585  - 19/Unknown 5s 225ms/step - accuracy: 0.8029 - loss: 769.1092 + 19/Unknown 4s 200ms/step - accuracy: 0.8470 - loss: 685.7762  - 20/Unknown 5s 226ms/step - accuracy: 0.8079 - loss: 756.8516 + 20/Unknown 4s 198ms/step - accuracy: 0.8505 - loss: 675.3044  - 21/Unknown 5s 225ms/step - accuracy: 0.8125 - loss: 745.5027 + 21/Unknown 5s 197ms/step - accuracy: 0.8537 - loss: 665.8409  - 22/Unknown 5s 223ms/step - accuracy: 0.8168 - loss: 734.6467 + 22/Unknown 5s 196ms/step - accuracy: 0.8566 - loss: 657.3629  - 23/Unknown 6s 222ms/step - accuracy: 0.8207 - loss: 724.4232 + 23/Unknown 5s 195ms/step - accuracy: 0.8593 - loss: 649.5444  - 24/Unknown 6s 222ms/step - accuracy: 0.8245 - loss: 714.9254 + 24/Unknown 5s 195ms/step - accuracy: 0.8618 - loss: 642.1780  - 25/Unknown 6s 221ms/step - accuracy: 0.8279 - loss: 706.1494 + 25/Unknown 5s 194ms/step - accuracy: 0.8641 - loss: 635.1900  - 26/Unknown 6s 220ms/step - accuracy: 0.8311 - loss: 697.8618 + 26/Unknown 6s 195ms/step - accuracy: 0.8662 - loss: 628.5919  - 27/Unknown 6s 219ms/step - accuracy: 0.8341 - loss: 690.1976 + 27/Unknown 6s 195ms/step - accuracy: 0.8683 - loss: 622.2363  - 28/Unknown 7s 218ms/step - accuracy: 0.8369 - loss: 682.8348 + 28/Unknown 6s 195ms/step - accuracy: 0.8702 - loss: 616.1565  - 29/Unknown 7s 218ms/step - accuracy: 0.8395 - loss: 675.9014 + 29/Unknown 6s 194ms/step - accuracy: 0.8720 - loss: 610.3881  - 30/Unknown 7s 220ms/step - accuracy: 0.8420 - loss: 669.2024 + 30/Unknown 6s 194ms/step - accuracy: 0.8737 - loss: 604.7990  - 31/Unknown 7s 219ms/step - accuracy: 0.8444 - loss: 662.8259 + 31/Unknown 6s 193ms/step - accuracy: 0.8753 - loss: 599.5613  - 32/Unknown 8s 219ms/step - accuracy: 0.8467 - loss: 656.7582 + 32/Unknown 7s 194ms/step - accuracy: 0.8769 - loss: 594.4847  - 33/Unknown 8s 219ms/step - accuracy: 0.8488 - loss: 650.9562 + 33/Unknown 7s 194ms/step - accuracy: 0.8783 - loss: 589.5745  - 34/Unknown 8s 218ms/step - accuracy: 0.8508 - loss: 645.4325 + 34/Unknown 7s 194ms/step - accuracy: 0.8797 - loss: 584.9431  - 35/Unknown 8s 218ms/step - accuracy: 0.8527 - loss: 640.0767 + 35/Unknown 7s 194ms/step - accuracy: 0.8810 - loss: 580.5197  - 36/Unknown 8s 218ms/step - accuracy: 0.8546 - loss: 634.9040 + 36/Unknown 7s 193ms/step - accuracy: 0.8822 - loss: 576.2609  - 37/Unknown 9s 217ms/step - accuracy: 0.8563 - loss: 629.9690 + 37/Unknown 8s 193ms/step - accuracy: 0.8834 - loss: 572.0708  - 38/Unknown 9s 216ms/step - accuracy: 0.8580 - loss: 625.1686 + 38/Unknown 8s 194ms/step - accuracy: 0.8845 - loss: 567.9126  - 39/Unknown 9s 215ms/step - accuracy: 0.8596 - loss: 620.4742 + 39/Unknown 8s 194ms/step - accuracy: 0.8856 - loss: 563.8269  - 40/Unknown 9s 215ms/step - accuracy: 0.8611 - loss: 616.0271 + 40/Unknown 8s 194ms/step - accuracy: 0.8867 - loss: 559.9911  - 41/Unknown 9s 215ms/step - accuracy: 0.8625 - loss: 611.7262 + 41/Unknown 8s 194ms/step - accuracy: 0.8877 - loss: 556.2637  - 42/Unknown 10s 215ms/step - accuracy: 0.8639 - loss: 607.5671 + 42/Unknown 9s 193ms/step - accuracy: 0.8886 - loss: 552.6080 - - 43/Unknown 10s 214ms/step - accuracy: 0.8653 - loss: 603.5233 + + 43/Unknown 9s 193ms/step - accuracy: 0.8896 - loss: 549.0726 - - 44/Unknown 10s 214ms/step - accuracy: 0.8666 - loss: 599.6008 + + 44/Unknown 9s 193ms/step - accuracy: 0.8905 - loss: 545.6210 - - 45/Unknown 10s 214ms/step - accuracy: 0.8679 - loss: 595.7900 + + 45/Unknown 9s 193ms/step - accuracy: 0.8913 - loss: 542.2662 - - 46/Unknown 10s 213ms/step - accuracy: 0.8691 - loss: 592.0447 + + 46/Unknown 9s 193ms/step - accuracy: 0.8921 - loss: 539.0649 - - 47/Unknown 11s 214ms/step - accuracy: 0.8702 - loss: 588.3965 + + 47/Unknown 10s 193ms/step - accuracy: 0.8929 - loss: 535.9783  - 48/Unknown 11s 214ms/step - accuracy: 0.8713 - loss: 584.8365 + 48/Unknown 10s 193ms/step - accuracy: 0.8936 - loss: 532.9994  - 49/Unknown 11s 214ms/step - accuracy: 0.8724 - loss: 581.3633 + 49/Unknown 10s 193ms/step - accuracy: 0.8944 - loss: 530.0856  - 50/Unknown 11s 215ms/step - accuracy: 0.8735 - loss: 577.9586 + 50/Unknown 10s 193ms/step - accuracy: 0.8951 - loss: 527.2556  - 51/Unknown 11s 215ms/step - accuracy: 0.8745 - loss: 574.6376 + 51/Unknown 10s 194ms/step - accuracy: 0.8957 - loss: 524.4853  - 52/Unknown 12s 215ms/step - accuracy: 0.8755 - loss: 571.4106 + 52/Unknown 11s 194ms/step - accuracy: 0.8964 - loss: 521.8221  - 53/Unknown 12s 215ms/step - accuracy: 0.8764 - loss: 568.2571 + 53/Unknown 11s 194ms/step - accuracy: 0.8970 - loss: 519.2384  - 54/Unknown 12s 215ms/step - accuracy: 0.8773 - loss: 565.1942 + 54/Unknown 11s 194ms/step - accuracy: 0.8976 - loss: 516.6887  - 55/Unknown 12s 215ms/step - accuracy: 0.8782 - loss: 562.1995 + 55/Unknown 11s 195ms/step - accuracy: 0.8982 - loss: 514.2283  - 56/Unknown 13s 215ms/step - accuracy: 0.8791 - loss: 559.2789 + 56/Unknown 11s 195ms/step - 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Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset. self._interrupted_warning() @@ -2633,7 +2483,7 @@ Start training the model...  ```
- 640/640 ━━━━━━━━━━━━━━━━━━━━ 174s 271ms/step - accuracy: 0.9358 - loss: 310.1415 - val_accuracy: 0.9498 - val_loss: 227.1810 + 639/639 ━━━━━━━━━━━━━━━━━━━━ 160s 249ms/step - accuracy: 0.9383 - loss: 301.8082 - val_accuracy: 0.9485 - val_loss: 235.7996
@@ -2646,1145 +2496,1145 @@ Evaluating model performance...
``` - 1/Unknown 0s 341ms/step - accuracy: 0.9509 - loss: 162.4458 + 1/Unknown 0s 331ms/step - accuracy: 0.9623 - loss: 160.6135  - 2/Unknown 0s 144ms/step - accuracy: 0.9443 - loss: 181.0830 + 2/Unknown 0s 119ms/step - accuracy: 0.9557 - loss: 181.4366  - 3/Unknown 1s 142ms/step - accuracy: 0.9419 - loss: 196.8291 + 3/Unknown 1s 131ms/step - accuracy: 0.9524 - loss: 198.4659  - 4/Unknown 1s 146ms/step - accuracy: 0.9404 - loss: 206.0489 + 4/Unknown 1s 129ms/step - accuracy: 0.9502 - loss: 209.3009  - 5/Unknown 1s 142ms/step - accuracy: 0.9407 - loss: 211.7518 + 5/Unknown 1s 133ms/step - accuracy: 0.9499 - loss: 215.6982  - 6/Unknown 1s 145ms/step - accuracy: 0.9415 - loss: 215.5974 + 6/Unknown 1s 131ms/step - accuracy: 0.9499 - loss: 219.7466  - 7/Unknown 1s 146ms/step - accuracy: 0.9426 - loss: 216.1428 + 7/Unknown 1s 132ms/step - accuracy: 0.9502 - loss: 220.2296  - 8/Unknown 1s 145ms/step - accuracy: 0.9435 - loss: 215.5485 + 8/Unknown 1s 132ms/step - accuracy: 0.9504 - loss: 219.6000  - 9/Unknown 2s 145ms/step - accuracy: 0.9441 - loss: 214.7674 + 9/Unknown 1s 133ms/step - accuracy: 0.9506 - loss: 218.5403  - 10/Unknown 2s 144ms/step - accuracy: 0.9446 - loss: 213.8222 + 10/Unknown 2s 133ms/step - accuracy: 0.9507 - loss: 217.4007  - 11/Unknown 2s 144ms/step - accuracy: 0.9452 - loss: 213.1922 + 11/Unknown 2s 134ms/step - accuracy: 0.9507 - loss: 216.4865  - 12/Unknown 2s 144ms/step - accuracy: 0.9455 - loss: 212.5974 + 12/Unknown 2s 133ms/step - accuracy: 0.9504 - loss: 215.7090  - 13/Unknown 2s 144ms/step - accuracy: 0.9457 - loss: 212.4554 + 13/Unknown 2s 135ms/step - accuracy: 0.9502 - loss: 215.4628  - 14/Unknown 2s 144ms/step - accuracy: 0.9459 - loss: 212.1609 + 14/Unknown 2s 135ms/step - accuracy: 0.9500 - loss: 215.0735  - 15/Unknown 2s 145ms/step - accuracy: 0.9461 - loss: 212.0100 + 15/Unknown 2s 134ms/step - accuracy: 0.9499 - loss: 214.8078  - 16/Unknown 3s 145ms/step - accuracy: 0.9464 - loss: 211.6899 + 16/Unknown 2s 134ms/step - accuracy: 0.9500 - loss: 214.3558  - 17/Unknown 3s 145ms/step - accuracy: 0.9466 - loss: 211.4328 + 17/Unknown 2s 134ms/step - accuracy: 0.9500 - loss: 213.9521  - 18/Unknown 3s 145ms/step - accuracy: 0.9468 - loss: 211.5108 + 18/Unknown 3s 135ms/step - accuracy: 0.9501 - loss: 213.9012  - 19/Unknown 3s 144ms/step - accuracy: 0.9469 - loss: 211.7577 + 19/Unknown 3s 134ms/step - accuracy: 0.9501 - loss: 214.0063  - 20/Unknown 3s 144ms/step - accuracy: 0.9470 - loss: 212.0558 + 20/Unknown 3s 135ms/step - accuracy: 0.9501 - loss: 214.2168  - 21/Unknown 3s 144ms/step - accuracy: 0.9470 - loss: 212.4518 + 21/Unknown 3s 134ms/step - accuracy: 0.9500 - loss: 214.5657  - 22/Unknown 3s 144ms/step - accuracy: 0.9471 - loss: 212.7789 + 22/Unknown 3s 135ms/step - accuracy: 0.9500 - loss: 214.8618  - 23/Unknown 4s 144ms/step - accuracy: 0.9471 - loss: 213.0560 + 23/Unknown 3s 134ms/step - accuracy: 0.9500 - loss: 215.1154  - 24/Unknown 4s 144ms/step - accuracy: 0.9471 - loss: 213.2825 + 24/Unknown 3s 135ms/step - accuracy: 0.9499 - loss: 215.2906  - 25/Unknown 4s 144ms/step - accuracy: 0.9471 - loss: 213.6518 + 25/Unknown 4s 134ms/step - accuracy: 0.9499 - loss: 215.6145  - 26/Unknown 4s 144ms/step - accuracy: 0.9471 - loss: 213.9233 + 26/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 215.8544  - 27/Unknown 4s 144ms/step - accuracy: 0.9472 - loss: 214.1503 + 27/Unknown 4s 135ms/step - accuracy: 0.9498 - loss: 216.0591  - 28/Unknown 4s 144ms/step - accuracy: 0.9472 - loss: 214.3847 + 28/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 216.2666  - 29/Unknown 4s 144ms/step - accuracy: 0.9473 - loss: 214.5778 + 29/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 216.4423  - 30/Unknown 5s 144ms/step - accuracy: 0.9473 - loss: 214.7275 + 30/Unknown 4s 135ms/step - accuracy: 0.9499 - loss: 216.5613  - 31/Unknown 5s 145ms/step - accuracy: 0.9473 - loss: 214.8989 + 31/Unknown 4s 135ms/step - accuracy: 0.9498 - loss: 216.7220  - 32/Unknown 5s 146ms/step - accuracy: 0.9474 - loss: 215.0651 + 32/Unknown 5s 135ms/step - accuracy: 0.9498 - loss: 216.8842  - 33/Unknown 5s 147ms/step - accuracy: 0.9474 - loss: 215.3561 + 33/Unknown 5s 135ms/step - accuracy: 0.9498 - loss: 217.1658  - 34/Unknown 5s 148ms/step - accuracy: 0.9474 - loss: 215.6748 + 34/Unknown 5s 135ms/step - accuracy: 0.9497 - loss: 217.4608  - 35/Unknown 5s 149ms/step - accuracy: 0.9473 - loss: 215.9542 + 35/Unknown 5s 135ms/step - accuracy: 0.9496 - loss: 217.7231  - 36/Unknown 6s 149ms/step - accuracy: 0.9473 - loss: 216.1981 + 36/Unknown 5s 134ms/step - accuracy: 0.9496 - loss: 217.9504  - 37/Unknown 6s 149ms/step - accuracy: 0.9473 - loss: 216.4192 + 37/Unknown 5s 135ms/step - accuracy: 0.9496 - loss: 218.1658  - 38/Unknown 6s 149ms/step - accuracy: 0.9473 - loss: 216.6240 + 38/Unknown 5s 134ms/step - accuracy: 0.9495 - loss: 218.3597  - 39/Unknown 6s 150ms/step - accuracy: 0.9473 - loss: 216.7979 + 39/Unknown 5s 134ms/step - accuracy: 0.9495 - loss: 218.5269  - 40/Unknown 6s 150ms/step - accuracy: 0.9474 - loss: 216.9769 + 40/Unknown 6s 135ms/step - accuracy: 0.9495 - loss: 218.7106  - 41/Unknown 6s 150ms/step - accuracy: 0.9474 - loss: 217.1346 + 41/Unknown 6s 135ms/step - accuracy: 0.9494 - loss: 218.8721  - 42/Unknown 7s 150ms/step - accuracy: 0.9474 - loss: 217.2511 + 42/Unknown 6s 136ms/step - accuracy: 0.9494 - loss: 218.9924  - 43/Unknown 7s 150ms/step - accuracy: 0.9474 - loss: 217.4261 + 43/Unknown 6s 136ms/step - accuracy: 0.9494 - loss: 219.1745  - 44/Unknown 7s 150ms/step - accuracy: 0.9474 - loss: 217.5872 + 44/Unknown 6s 137ms/step - accuracy: 0.9494 - loss: 219.3449  - 45/Unknown 7s 150ms/step - accuracy: 0.9474 - loss: 217.7351 + 45/Unknown 6s 138ms/step - accuracy: 0.9493 - loss: 219.4943  - 46/Unknown 7s 150ms/step - accuracy: 0.9474 - loss: 217.8584 + 46/Unknown 7s 137ms/step - accuracy: 0.9493 - loss: 219.6201  - 47/Unknown 7s 150ms/step - accuracy: 0.9474 - loss: 217.9560 + 47/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 219.7240  - 48/Unknown 7s 149ms/step - accuracy: 0.9475 - loss: 218.0626 + 48/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 219.8335  - 49/Unknown 7s 149ms/step - accuracy: 0.9475 - loss: 218.1622 + 49/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 219.9367  - 50/Unknown 8s 148ms/step - accuracy: 0.9475 - loss: 218.3029 + 50/Unknown 7s 138ms/step - accuracy: 0.9493 - loss: 220.0834  - 51/Unknown 8s 148ms/step - accuracy: 0.9475 - loss: 218.4238 + 51/Unknown 7s 138ms/step - accuracy: 0.9492 - loss: 220.2067  - 52/Unknown 8s 148ms/step - accuracy: 0.9475 - loss: 218.5109 + 52/Unknown 7s 139ms/step - accuracy: 0.9492 - loss: 220.2963  - 53/Unknown 8s 147ms/step - accuracy: 0.9476 - loss: 218.5741 + 53/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.3649  - 54/Unknown 8s 147ms/step - accuracy: 0.9476 - loss: 218.6540 + 54/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.4462  - 55/Unknown 8s 147ms/step - accuracy: 0.9476 - loss: 218.7485 + 55/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.5459  - 56/Unknown 8s 146ms/step - accuracy: 0.9476 - loss: 218.8147 + 56/Unknown 8s 138ms/step - accuracy: 0.9492 - loss: 220.6197  - 57/Unknown 9s 146ms/step - accuracy: 0.9477 - loss: 218.8586 + 57/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.6727  - 58/Unknown 9s 146ms/step - accuracy: 0.9477 - loss: 218.9425 + 58/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.7652  - 59/Unknown 9s 146ms/step - accuracy: 0.9477 - loss: 219.0194 + 59/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.8513  - 60/Unknown 9s 146ms/step - accuracy: 0.9477 - loss: 219.0956 + 60/Unknown 8s 138ms/step - accuracy: 0.9491 - loss: 220.9392  - 61/Unknown 9s 146ms/step - accuracy: 0.9477 - loss: 219.1801 + 61/Unknown 9s 138ms/step - accuracy: 0.9491 - loss: 221.0330  - 62/Unknown 9s 146ms/step - accuracy: 0.9478 - loss: 219.2492 + 62/Unknown 9s 137ms/step - accuracy: 0.9491 - loss: 221.1127  - 63/Unknown 9s 146ms/step - accuracy: 0.9478 - loss: 219.3433 + 63/Unknown 9s 137ms/step - accuracy: 0.9491 - loss: 221.2177  - 64/Unknown 10s 146ms/step - accuracy: 0.9478 - loss: 219.4519 + 64/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.3382 - - 65/Unknown 10s 146ms/step - accuracy: 0.9478 - loss: 219.5580 + + 65/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.4572 - - 66/Unknown 10s 146ms/step - accuracy: 0.9478 - loss: 219.6452 + + 66/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.5552 - - 67/Unknown 10s 145ms/step - accuracy: 0.9478 - loss: 219.7370 + + 67/Unknown 9s 137ms/step - accuracy: 0.9490 - loss: 221.6626 - - 68/Unknown 10s 145ms/step - accuracy: 0.9478 - loss: 219.8202 + + 68/Unknown 9s 136ms/step - accuracy: 0.9490 - loss: 221.7653 - - 69/Unknown 10s 145ms/step - accuracy: 0.9478 - loss: 219.9068 + + 69/Unknown 10s 136ms/step - accuracy: 0.9490 - loss: 221.8680  - 70/Unknown 10s 145ms/step - accuracy: 0.9479 - loss: 219.9808 + 70/Unknown 10s 136ms/step - accuracy: 0.9490 - loss: 221.9582  - 71/Unknown 11s 145ms/step - accuracy: 0.9479 - loss: 220.0469 + 71/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.0398  - 72/Unknown 11s 145ms/step - accuracy: 0.9479 - loss: 220.1314 + 72/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.1409  - 73/Unknown 11s 145ms/step - accuracy: 0.9479 - loss: 220.2233 + 73/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.2496  - 74/Unknown 11s 145ms/step - accuracy: 0.9479 - loss: 220.3074 + 74/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.3526  - 75/Unknown 11s 145ms/step - accuracy: 0.9479 - loss: 220.3792 + 75/Unknown 10s 136ms/step - accuracy: 0.9489 - loss: 222.4433  - 76/Unknown 11s 145ms/step - accuracy: 0.9479 - loss: 220.4452 + 76/Unknown 11s 136ms/step - accuracy: 0.9489 - loss: 222.5272  - 77/Unknown 11s 145ms/step - accuracy: 0.9480 - loss: 220.5054 + 77/Unknown 11s 136ms/step - accuracy: 0.9489 - loss: 222.6031  - 78/Unknown 12s 145ms/step - accuracy: 0.9480 - loss: 220.5729 + 78/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.6857  - 79/Unknown 12s 145ms/step - accuracy: 0.9480 - loss: 220.6319 + 79/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.7623  - 80/Unknown 12s 146ms/step - accuracy: 0.9480 - loss: 220.6857 + 80/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.8322  - 81/Unknown 12s 146ms/step - accuracy: 0.9480 - loss: 220.7341 + 81/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.8963  - 82/Unknown 12s 147ms/step - accuracy: 0.9480 - loss: 220.7907 + 82/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 222.9694  - 83/Unknown 12s 147ms/step - accuracy: 0.9480 - loss: 220.8492 + 83/Unknown 11s 136ms/step - accuracy: 0.9488 - loss: 223.0455  - 84/Unknown 13s 147ms/step - accuracy: 0.9480 - loss: 220.9068 + 84/Unknown 12s 136ms/step - accuracy: 0.9488 - loss: 223.1209  - 85/Unknown 13s 147ms/step - accuracy: 0.9480 - loss: 220.9686 + 85/Unknown 12s 136ms/step - accuracy: 0.9488 - loss: 223.1990  - 86/Unknown 13s 147ms/step - accuracy: 0.9480 - loss: 221.0368 + 86/Unknown 12s 136ms/step - accuracy: 0.9488 - loss: 223.2825  - 87/Unknown 13s 147ms/step - accuracy: 0.9480 - loss: 221.1018 + 87/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.3633  - 88/Unknown 13s 147ms/step - accuracy: 0.9480 - loss: 221.1624 + 88/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.4366  - 89/Unknown 13s 148ms/step - accuracy: 0.9480 - loss: 221.2261 + 89/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.5151  - 90/Unknown 13s 148ms/step - accuracy: 0.9481 - loss: 221.2927 + 90/Unknown 12s 136ms/step - accuracy: 0.9487 - loss: 223.5958  - 91/Unknown 14s 148ms/step - accuracy: 0.9481 - loss: 221.3554 + 91/Unknown 13s 136ms/step - accuracy: 0.9487 - loss: 223.6727  - 92/Unknown 14s 148ms/step - accuracy: 0.9481 - loss: 221.4201 + 92/Unknown 13s 137ms/step - accuracy: 0.9487 - loss: 223.7505  - 93/Unknown 14s 148ms/step - accuracy: 0.9481 - loss: 221.4830 + 93/Unknown 13s 137ms/step - accuracy: 0.9487 - loss: 223.8250  - 94/Unknown 14s 148ms/step - accuracy: 0.9481 - loss: 221.5580 + 94/Unknown 13s 137ms/step - accuracy: 0.9486 - loss: 223.9114  - 95/Unknown 14s 148ms/step - accuracy: 0.9481 - loss: 221.6310 + 95/Unknown 13s 137ms/step - accuracy: 0.9486 - loss: 223.9948  - 96/Unknown 14s 148ms/step - accuracy: 0.9481 - loss: 221.7074 + 96/Unknown 13s 138ms/step - accuracy: 0.9486 - loss: 224.0807  - 97/Unknown 15s 147ms/step - accuracy: 0.9481 - loss: 221.7751 + 97/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.1586  - 98/Unknown 15s 147ms/step - accuracy: 0.9481 - loss: 221.8373 + 98/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.2289  - 99/Unknown 15s 148ms/step - accuracy: 0.9481 - loss: 221.9001 + 99/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.2979  -100/Unknown 15s 148ms/step - accuracy: 0.9481 - loss: 221.9738 +100/Unknown 14s 138ms/step - accuracy: 0.9486 - loss: 224.3739  -101/Unknown 15s 148ms/step - accuracy: 0.9481 - loss: 222.0465 +101/Unknown 14s 138ms/step - accuracy: 0.9485 - loss: 224.4488  -102/Unknown 15s 148ms/step - accuracy: 0.9480 - loss: 222.1164 +102/Unknown 14s 138ms/step - accuracy: 0.9485 - loss: 224.5210  -103/Unknown 15s 148ms/step - accuracy: 0.9480 - loss: 222.1863 +103/Unknown 14s 139ms/step - accuracy: 0.9485 - loss: 224.5936  -104/Unknown 16s 148ms/step - accuracy: 0.9480 - loss: 222.2534 +104/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.6630  -105/Unknown 16s 148ms/step - accuracy: 0.9480 - loss: 222.3202 +105/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.7316  -106/Unknown 16s 148ms/step - accuracy: 0.9480 - loss: 222.3874 +106/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.8002  -107/Unknown 16s 148ms/step - accuracy: 0.9480 - loss: 222.4603 +107/Unknown 15s 139ms/step - accuracy: 0.9485 - loss: 224.8736  -108/Unknown 16s 148ms/step - accuracy: 0.9480 - loss: 222.5335 +108/Unknown 15s 139ms/step - accuracy: 0.9484 - loss: 224.9466  -109/Unknown 16s 148ms/step - accuracy: 0.9480 - loss: 222.6129 +109/Unknown 15s 138ms/step - accuracy: 0.9484 - loss: 225.0268  -110/Unknown 17s 149ms/step - accuracy: 0.9480 - loss: 222.6919 +110/Unknown 15s 138ms/step - accuracy: 0.9484 - loss: 225.1065  -111/Unknown 17s 148ms/step - accuracy: 0.9480 - loss: 222.7742 +111/Unknown 16s 138ms/step - accuracy: 0.9484 - loss: 225.1895  -112/Unknown 17s 148ms/step - accuracy: 0.9480 - loss: 222.8567 +112/Unknown 16s 139ms/step - 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accuracy: 0.9485 - loss: 229.1717  -369/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2889 +369/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1778  -370/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2951 +370/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1837  -371/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3009 +371/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1893  -372/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3066 +372/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1947  -373/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3122 +373/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1999  -374/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3181 +374/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.2054  -375/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3240 +375/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.2110  -376/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3297 +376/Unknown 53s 139ms/step - accuracy: 0.9486 - loss: 229.2163  -377/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3355 +377/Unknown 53s 139ms/step - accuracy: 0.9486 - loss: 229.2217  ```
- 377/377 ━━━━━━━━━━━━━━━━━━━━ 56s 149ms/step - accuracy: 0.9490 - loss: 227.3412 + 377/377 ━━━━━━━━━━━━━━━━━━━━ 53s 139ms/step - accuracy: 0.9486 - loss: 229.2270
``` -Test accuracy: 95.0% +Test accuracy: 94.94% ```
From bec3344b9a3b8dbc31a854789a8a3ef871506cad Mon Sep 17 00:00:00 2001 From: Humbulani Date: Thu, 16 Jan 2025 23:31:16 +0200 Subject: [PATCH 5/6] addressing comments for classification_with_grn_and _vsn.py --- examples/structured_data/classification_with_grn_and_vsn.py | 2 +- examples/structured_data/md/classification_with_grn_and_vsn.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index 39611755aa..1209cb137b 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -471,7 +471,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 1 # may be adjusted to a desired value +num_epochs = 20 # may be adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) diff --git a/examples/structured_data/md/classification_with_grn_and_vsn.md b/examples/structured_data/md/classification_with_grn_and_vsn.md index 6bd5bd9539..43cbc247ff 100644 --- a/examples/structured_data/md/classification_with_grn_and_vsn.md +++ b/examples/structured_data/md/classification_with_grn_and_vsn.md @@ -509,7 +509,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 1 # may be adjusted to a desired value +num_epochs = 20 # may be adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) From 290dd6064903ac82801cf2b88fa68aa4197cd1c1 Mon Sep 17 00:00:00 2001 From: Humbulani Date: Thu, 16 Jan 2025 23:32:07 +0200 Subject: [PATCH 6/6] addressing comments for classification_with_grn_and _vsn.py --- .../structured_data/ipynb/classification_with_grn_and_vsn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb index 40aa463bb8..bd97a3e40d 100644 --- a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb +++ b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb @@ -681,7 +681,7 @@ "learning_rate = 0.001\n", "dropout_rate = 0.15\n", "batch_size = 265\n", - "num_epochs = 1 # may be adjusted to a desired value\n", + "num_epochs = 20 # may be adjusted to a desired value\n", "encoding_size = 16\n", "\n", "model = create_model(encoding_size)\n",