diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index e0d4cde613..21bbab3ac2 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -2,7 +2,7 @@ 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: 2025/01/03 Description: Using Gated Residual and Variable Selection Networks for income level prediction. Accelerator: GPU """ @@ -46,6 +46,8 @@ """ import os +import subprocess +import tarfile # Only the TensorFlow backend supports string inputs. os.environ["KERAS_BACKEND"] = "tensorflow" @@ -108,13 +110,37 @@ "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 +183,21 @@ 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 @@ -219,10 +260,10 @@ def process(features, target): features[feature_name] = keras.ops.cast(features[feature_name], "string") # Get the instance weight. weight = features.pop(WEIGHT_COLUMN_NAME) - return features, target, weight + return dict(features), target, weight -def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128): +def get_dataset_from_csv(csv_file_path, batch_size, shuffle=False): dataset = tf.data.experimental.make_csv_dataset( csv_file_path, batch_size=batch_size, @@ -277,7 +318,7 @@ def encode_inputs(inputs, encoding_size): # 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 + vocabulary=vocabulary, mask_token=None, num_oov_indices=1 ) # Convert the string input values into integer indices. value_index = index(inputs[feature_name]) @@ -312,6 +353,10 @@ def __init__(self, units): def call(self, inputs): return self.linear(inputs) * self.sigmoid(inputs) + # to remove the build warnings + def build(self): + self.built = True + """ ## Implement the Gated Residual Network @@ -347,6 +392,10 @@ def call(self, inputs): x = self.layer_norm(x) return x + # to remove the build warnings + def build(self): + self.build = True + """ ## Implement the Variable Selection Network @@ -388,6 +437,10 @@ def call(self, inputs): outputs = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) return outputs + # to remove the build warnings + def build(self): + self.built = True + """ ## Create Gated Residual and Variable Selection Networks model @@ -415,7 +468,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 20 +num_epochs = 1 encoding_size = 16 model = create_model(encoding_size) @@ -433,7 +486,9 @@ def create_model(encoding_size): print("Start training the model...") train_dataset = get_dataset_from_csv( - train_data_file, shuffle=True, batch_size=batch_size + train_data_file, + batch_size=batch_size, + shuffle=True, ) valid_dataset = get_dataset_from_csv(valid_data_file, batch_size=batch_size) model.fit( 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..0ec68ec9dd 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:** 2025/01/03
\n", "**Description:** Using Gated Residual and Variable Selection Networks for income level prediction." ] }, @@ -76,6 +76,8 @@ "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", @@ -152,11 +154,55 @@ " \"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", @@ -235,7 +281,36 @@ "\n", "train_data.to_csv(train_data_file, index=False, header=False)\n", "valid_data.to_csv(valid_data_file, index=False, header=False)\n", - "test_data.to_csv(test_data_file, index=False, header=False)" + "test_data.to_csv(test_data_file, index=False, header=False)\n", + "" + ] + }, + { + "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", + ")" ] }, { @@ -288,9 +363,12 @@ ")\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", "]" ] @@ -324,10 +402,10 @@ " features[feature_name] = keras.ops.cast(features[feature_name], \"string\")\n", " # Get the instance weight.\n", " weight = features.pop(WEIGHT_COLUMN_NAME)\n", - " return features, target, weight\n", + " return dict(features), target, weight\n", "\n", "\n", - "def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128):\n", + "def get_dataset_from_csv(csv_file_path, batch_size, shuffle=False):\n", " dataset = tf.data.experimental.make_csv_dataset(\n", " csv_file_path,\n", " batch_size=batch_size,\n", @@ -409,7 +487,7 @@ " # 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", + " vocabulary=vocabulary, mask_token=None, num_oov_indices=1\n", " )\n", " # Convert the string input values into integer indices.\n", " value_index = index(inputs[feature_name])\n", @@ -457,6 +535,10 @@ "\n", " def call(self, inputs):\n", " return self.linear(inputs) * self.sigmoid(inputs)\n", + "\n", + " # to remove the build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, @@ -506,6 +588,10 @@ " x = inputs + self.gated_linear_unit(x)\n", " x = self.layer_norm(x)\n", " return x\n", + "\n", + " # to remove the build warnings\n", + " def build(self):\n", + " self.build = True\n", "" ] }, @@ -561,6 +647,10 @@ "\n", " outputs = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)\n", " return outputs\n", + "\n", + " # to remove the build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, @@ -617,7 +707,7 @@ "learning_rate = 0.001\n", "dropout_rate = 0.15\n", "batch_size = 265\n", - "num_epochs = 20\n", + "num_epochs = 1\n", "encoding_size = 16\n", "\n", "model = create_model(encoding_size)\n", @@ -635,7 +725,9 @@ "\n", "print(\"Start training the model...\")\n", "train_dataset = get_dataset_from_csv(\n", - " train_data_file, shuffle=True, batch_size=batch_size\n", + " train_data_file,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", ")\n", "valid_dataset = get_dataset_from_csv(valid_data_file, batch_size=batch_size)\n", "model.fit(\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..f53bec2cbd 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:** 2025/01/03
**Description:** Using Gated Residual and Variable Selection Networks for income level prediction. @@ -47,12 +47,18 @@ and 34 categorical features. ```python -import math +import os +import subprocess +import tarfile + +# Only the TensorFlow backend supports string inputs. +os.environ["KERAS_BACKEND"] = "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 +114,51 @@ 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}") @@ -160,8 +206,33 @@ test_data_file = "test_data.csv" train_data.to_csv(train_data_file, index=False, header=False) 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 +271,12 @@ 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 +290,18 @@ training and evaluation. ```python -from tensorflow.keras.layers import StringLookup - 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] = keras.ops.cast(features[feature_name], "string") # Get the instance weight. weight = features.pop(WEIGHT_COLUMN_NAME) - return features, target, weight + return dict(features), target, weight -def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128): - +def get_dataset_from_csv(csv_file_path, batch_size, shuffle=False): dataset = tf.data.experimental.make_csv_dataset( csv_file_path, batch_size=batch_size, @@ -257,11 +328,11 @@ def create_model_inputs(): for feature_name in FEATURE_NAMES: if feature_name in NUMERIC_FEATURE_NAMES: inputs[feature_name] = layers.Input( - name=feature_name, shape=(), dtype=tf.float32 + name=feature_name, shape=(), dtype="float32" ) else: inputs[feature_name] = layers.Input( - name=feature_name, shape=(), dtype=tf.string + name=feature_name, shape=(), dtype="string" ) return inputs @@ -287,8 +358,8 @@ def encode_inputs(inputs, encoding_size): # 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 + index = layers.StringLookup( + vocabulary=vocabulary, mask_token=None, num_oov_indices=1 ) # Convert the string input values into integer indices. value_index = index(inputs[feature_name]) @@ -300,7 +371,7 @@ def encode_inputs(inputs, encoding_size): 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 = 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 @@ -325,6 +396,10 @@ class GatedLinearUnit(layers.Layer): def call(self, inputs): return self.linear(inputs) * self.sigmoid(inputs) + # to remove the build warnings + def build(self): + self.built = True + ``` --- @@ -362,6 +437,10 @@ class GatedResidualNetwork(layers.Layer): x = self.layer_norm(x) return x + # to remove the build warnings + def build(self): + self.build = True + ``` --- @@ -395,16 +474,20 @@ class VariableSelection(layers.Layer): def call(self, inputs): v = layers.concatenate(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): x.append(self.grns[idx](input)) - x = tf.stack(x, axis=1) + x = keras.ops.stack(x, axis=1) - outputs = tf.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) + outputs = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) return outputs + # to remove the build warnings + def build(self): + self.built = True + ``` --- @@ -436,7 +519,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 20 +num_epochs = 1 encoding_size = 16 model = create_model(encoding_size) @@ -448,13 +531,15 @@ model.compile( # 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 ) print("Start training the model...") train_dataset = get_dataset_from_csv( - train_data_file, shuffle=True, batch_size=batch_size + train_data_file, + batch_size=batch_size, + shuffle=True, ) valid_dataset = get_dataset_from_csv(valid_data_file, batch_size=batch_size) model.fit( @@ -473,52 +558,3093 @@ 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% + +``` +
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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() + + +``` +
+ 641/641 ━━━━━━━━━━━━━━━━━━━━ 3383s 5s/step - accuracy: 0.9386 - loss: 288.5208 - val_accuracy: 0.9500 - val_loss: 230.6449 + + +
+``` +Model training finished. +Evaluating model performance... + +``` +
+ +
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+ 377/377 ━━━━━━━━━━━━━━━━━━━━ 314s 832ms/step - accuracy: 0.9490 - loss: 229.8986 + + +
+``` +Test accuracy: 94.95% ```