Keras documentation: Structured data classification from scratch (original) (raw)

Author: fchollet
Date created: 2020/06/09
Last modified: 2020/06/09
Description: Binary classification of structured data including numerical and categorical features.

View in Colab • GitHub source

Introduction

This example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categorical features. We will use Keras preprocessing layers to normalize the numerical features and vectorize the categorical ones.

Note that this example should be run with TensorFlow 2.5 or higher.

The dataset

Our dataset is provided by the Cleveland Clinic Foundation for Heart Disease. It's a CSV file with 303 rows. Each row contains information about a patient (asample), and each column describes an attribute of the patient (a feature). We use the features to predict whether a patient has a heart disease (binary classification).

Here's the description of each feature:

Setup

`import os

os.environ["KERAS_BACKEND"] = "torch" # or torch, or tensorflow

import pandas as pd import keras from keras import layers `

Preparing the data

Let's download the data and load it into a Pandas dataframe:

file_url = "http://storage.googleapis.com/download.tensorflow.org/data/heart.csv" dataframe = pd.read_csv(file_url)

The dataset includes 303 samples with 14 columns per sample (13 features, plus the target label):

Here's a preview of a few samples:

The last column, "target", indicates whether the patient has a heart disease (1) or not (0).

Let's split the data into a training and validation set:

`val_dataframe = dataframe.sample(frac=0.2, random_state=1337) train_dataframe = dataframe.drop(val_dataframe.index)

print( f"Using {len(train_dataframe)} samples for training " f"and {len(val_dataframe)} for validation" ) `

Using 242 samples for training and 61 for validation

Here, we define the metadata of the dataset that will be useful for reading and parsing the data into input features, and encoding the input features with respect to their types.

`COLUMN_NAMES = [ "age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", "exang", "oldpeak", "slope", "ca", "thal", "target", ]

Target feature name.

TARGET_FEATURE_NAME = "target"

Numeric feature names.

NUMERIC_FEATURE_NAMES = ["age", "trestbps", "thalach", "oldpeak", "slope", "chol"]

Categorical features and their vocabulary lists.

Note that we add 'v=' as a prefix to all categorical feature values to make

sure that they are treated as strings.

CATEGORICAL_FEATURES_WITH_VOCABULARY = { feature_name: sorted( [ # Integer categorcal must be int and string must be str value if dataframe[feature_name].dtype == "int64" else str(value) for value in list(dataframe[feature_name].unique()) ] ) for feature_name in COLUMN_NAMES if feature_name not in list(NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME]) }

All features names.

FEATURE_NAMES = NUMERIC_FEATURE_NAMES + list( CATEGORICAL_FEATURES_WITH_VOCABULARY.keys() ) `

Feature preprocessing with Keras layers

The following features are categorical features encoded as integers:

• sex
• cp
• fbs
• restecg
• exang
• ca

We will encode these features using one-hot encoding. We have two options here:

• Use CategoryEncoding(), which requires knowing the range of input values and will error on input outside the range.
• Use IntegerLookup() which will build a lookup table for inputs and reserve an output index for unkown input values.

For this example, we want a simple solution that will handle out of range inputs at inference, so we will use IntegerLookup().

We also have a categorical feature encoded as a string: thal. We will create an index of all possible features and encode output using the StringLookup() layer.

Finally, the following feature are continuous numerical features:

• age
• trestbps
• chol
• thalach
• oldpeak
• slope

For each of these features, we will use a Normalization() layer to make sure the mean of each feature is 0 and its standard deviation is 1.

Below, we define 2 utility functions to do the operations:

• encode_numerical_feature to apply featurewise normalization to numerical features.
• process to one-hot encode string or integer categorical features.

`# Tensorflow required for tf.data.Dataset 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 encode_categorical(features, target): for feature_name in features: if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY: lookup_class = ( layers.StringLookup if features[feature_name].dtype == "string" else layers.IntegerLookup ) 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 = lookup_class( vocabulary=vocabulary, mask_token=None, num_oov_indices=0, output_mode="binary", ) # Convert the string input values into integer indices. value_index = index(features[feature_name]) features[feature_name] = value_index

    else:
        pass

# Change features from OrderedDict to Dict to match Inputs as they are Dict.
return dict(features), target

def encode_numerical_feature(feature, name, dataset): # Create a Normalization layer for our feature normalizer = layers.Normalization() # Prepare a Dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the statistics of the data normalizer.adapt(feature_ds) # Normalize the input feature encoded_feature = normalizer(feature) return encoded_feature `

Let's generate tf.data.Dataset objects for each dataframe:

`def dataframe_to_dataset(dataframe): dataframe = dataframe.copy() labels = dataframe.pop("target") ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels)).map( encode_categorical ) ds = ds.shuffle(buffer_size=len(dataframe)) return ds

train_ds = dataframe_to_dataset(train_dataframe) val_ds = dataframe_to_dataset(val_dataframe) `

Each Dataset yields a tuple (input, target) where input is a dictionary of features and target is the value 0 or 1:

for x, y in train_ds.take(1): print("Input:", x) print("Target:", y)

Input: {'age': <tf.Tensor: shape=(), dtype=int64, numpy=45>, 'sex': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([0, 1])>, 'cp': <tf.Tensor: shape=(5,), dtype=int64, numpy=array([0, 0, 0, 0, 1])>, 'trestbps': <tf.Tensor: shape=(), dtype=int64, numpy=142>, 'chol': <tf.Tensor: shape=(), dtype=int64, numpy=309>, 'fbs': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 0])>, 'restecg': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([0, 0, 1])>, 'thalach': <tf.Tensor: shape=(), dtype=int64, numpy=147>, 'exang': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([0, 1])>, 'oldpeak': <tf.Tensor: shape=(), dtype=float64, numpy=0.0>, 'slope': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'ca': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 0, 0, 1])>, 'thal': <tf.Tensor: shape=(5,), dtype=int64, numpy=array([0, 0, 0, 0, 1])>} Target: tf.Tensor(1, shape=(), dtype=int64)

Let's batch the datasets:

train_ds = train_ds.batch(32) val_ds = val_ds.batch(32)

Build a model

With this done, we can create our end-to-end model:

`# Categorical features have different shapes after the encoding, dependent on the

vocabulary or unique values of each feature. We create them accordinly to match the

input data elements generated by tf.data.Dataset after pre-processing them

def create_model_inputs(): inputs = {}

# This a helper function for creating categorical features
def create_input_helper(feature_name):
    num_categories = len(CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name])
    inputs[feature_name] = layers.Input(
        name=feature_name, shape=(num_categories,), dtype="int64"
    )
    return inputs

for feature_name in FEATURE_NAMES:
    if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
        # Categorical features
        create_input_helper(feature_name)
    else:
        # Make them float32, they are Real numbers
        feature_input = layers.Input(name=feature_name, shape=(1,), dtype="float32")
        # Process the Inputs here
        inputs[feature_name] = encode_numerical_feature(
            feature_input, feature_name, train_ds
        )
return inputs

This Layer defines the logic of the Model to perform the classification

class Classifier(keras.layers.Layer):

def __init__(self, **kwargs):
    super().__init__(**kwargs)
    self.dense_1 = layers.Dense(32, activation="relu")
    self.dropout = layers.Dropout(0.5)
    self.dense_2 = layers.Dense(1, activation="sigmoid")

def call(self, inputs):
    all_features = layers.concatenate(list(inputs.values()))
    x = self.dense_1(all_features)
    x = self.dropout(x)
    output = self.dense_2(x)
    return output

# Surpress build warnings
def build(self, input_shape):
    self.built = True

Create the Classifier model

def create_model(): all_inputs = create_model_inputs() output = Classifier()(all_inputs) model = keras.Model(all_inputs, output) return model

model = create_model() model.compile("adam", "binary_crossentropy", metrics=["accuracy"]) `

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py:106: UserWarning: When providing `inputs` as a dict, all keys in the dict must match the names of the corresponding tensors. Received key 'age' mapping to value <KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor> which has name 'keras_tensor'. Change the tensor name to 'age' (via `Input(..., name='age')`) warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py:106: UserWarning: When providing `inputs` as a dict, all keys in the dict must match the names of the corresponding tensors. Received key 'trestbps' mapping to value <KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_1> which has name 'keras_tensor_1'. Change the tensor name to 'trestbps' (via `Input(..., name='trestbps')`) warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py:106: UserWarning: When providing `inputs` as a dict, all keys in the dict must match the names of the corresponding tensors. Received key 'thalach' mapping to value <KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_2> which has name 'keras_tensor_2'. Change the tensor name to 'thalach' (via `Input(..., name='thalach')`) warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py:106: UserWarning: When providing `inputs` as a dict, all keys in the dict must match the names of the corresponding tensors. Received key 'oldpeak' mapping to value <KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_3> which has name 'keras_tensor_3'. Change the tensor name to 'oldpeak' (via `Input(..., name='oldpeak')`) warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py:106: UserWarning: When providing `inputs` as a dict, all keys in the dict must match the names of the corresponding tensors. Received key 'slope' mapping to value <KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_4> which has name 'keras_tensor_4'. Change the tensor name to 'slope' (via `Input(..., name='slope')`) warnings.warn( /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py:106: UserWarning: When providing `inputs` as a dict, all keys in the dict must match the names of the corresponding tensors. Received key 'chol' mapping to value <KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_5> which has name 'keras_tensor_5'. Change the tensor name to 'chol' (via `Input(..., name='chol')`) warnings.warn(

Let's visualize our connectivity graph:

# `rankdir='LR'` is to make the graph horizontal. keras.utils.plot_model(model, show_shapes=True, rankdir="LR")

Train the model

model.fit(train_ds, epochs=50, validation_data=val_ds)

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6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6782 - loss: 4.7529



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.6627 - loss: 5.0219 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 127ms/step - accuracy: 0.6875 - loss: 5.0149



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6342 - loss: 5.5898



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - accuracy: 0.6290 - loss: 5.6701 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 121ms/step - accuracy: 0.5938 - loss: 6.0783



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6259 - loss: 5.6908



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.6352 - loss: 5.5719 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 112ms/step - accuracy: 0.7812 - loss: 3.1021



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7353 - loss: 3.8725



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.7163 - loss: 4.1637 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 112ms/step - accuracy: 0.5625 - loss: 6.9224



5/8 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.6331 - loss: 5.5663



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.6416 - loss: 5.4024 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 117ms/step - accuracy: 0.6875 - loss: 4.4043



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6668 - loss: 5.0742



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6743 - loss: 4.9986 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 104ms/step - accuracy: 0.6562 - loss: 5.3405



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.6868 - loss: 4.7990



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6838 - loss: 4.8458 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 116ms/step - accuracy: 0.6562 - loss: 4.8092



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7061 - loss: 4.3996



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.7053 - loss: 4.4297 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 114ms/step - accuracy: 0.6250 - loss: 5.6655



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6536 - loss: 5.3912



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.6589 - loss: 5.3014 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 116ms/step - accuracy: 0.7812 - loss: 3.5258



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.6900 - loss: 4.7711



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6882 - loss: 4.8074 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 123ms/step - accuracy: 0.5938 - loss: 6.5425



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6346 - loss: 5.6779



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6423 - loss: 5.5672 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 120ms/step - accuracy: 0.6250 - loss: 5.6215



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6451 - loss: 5.2140



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.6556 - loss: 5.0993 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 115ms/step - accuracy: 0.7188 - loss: 4.2096



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7218 - loss: 4.3075



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.7143 - loss: 4.4143 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 114ms/step - accuracy: 0.5625 - loss: 7.0242



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6608 - loss: 5.3428



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.6675 - loss: 5.2031 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 105ms/step - accuracy: 0.6875 - loss: 5.0369



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6601 - loss: 5.2386



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.6675 - loss: 5.0972 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 114ms/step - accuracy: 0.6562 - loss: 4.8957



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7086 - loss: 4.4144



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.6980 - loss: 4.5912 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 115ms/step - accuracy: 0.6250 - loss: 6.0333



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6438 - loss: 5.6852



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.6551 - loss: 5.4504 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 119ms/step - accuracy: 0.5938 - loss: 6.4043



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6659 - loss: 5.2220



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6751 - loss: 5.0637 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 122ms/step - accuracy: 0.5625 - loss: 7.0517



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6782 - loss: 5.0396



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6854 - loss: 4.9129 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 121ms/step - accuracy: 0.6562 - loss: 5.4278



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6575 - loss: 5.2183



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6676 - loss: 5.0430 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 120ms/step - accuracy: 0.7500 - loss: 3.9611



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7322 - loss: 4.2233



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.7325 - loss: 4.2274 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 127ms/step - accuracy: 0.8438 - loss: 2.5075



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7483 - loss: 3.8605



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step - accuracy: 0.7305 - loss: 4.1423 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 132ms/step - accuracy: 0.7188 - loss: 4.5277



5/8 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.6698 - loss: 5.2541



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.6831 - loss: 4.9995 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 1s 149ms/step - accuracy: 0.7188 - loss: 4.3368



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6884 - loss: 4.8941



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step - accuracy: 0.6877 - loss: 4.9237 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 113ms/step - accuracy: 0.7188 - loss: 3.6048



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.6953 - loss: 4.5189



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.6914 - loss: 4.6078 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 120ms/step - accuracy: 0.7188 - loss: 4.5277



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7298 - loss: 4.2710



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - accuracy: 0.7214 - loss: 4.4175 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 117ms/step - accuracy: 0.7500 - loss: 4.0295



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6962 - loss: 4.8892



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step - accuracy: 0.6981 - loss: 4.8478 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 122ms/step - accuracy: 0.7812 - loss: 3.4540



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7095 - loss: 4.5553



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.7080 - loss: 4.5585 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 117ms/step - accuracy: 0.6875 - loss: 4.5707



7/8 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6914 - loss: 4.7756



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6939 - loss: 4.7445 - val_accuracy: 0.7705 - val_loss: 3.6992

1/8 ━━━━━━━━━━━━━━━━━━━━ 0s 124ms/step - accuracy: 0.7188 - loss: 4.0735



6/8 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7049 - loss: 4.3802



8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.6987 - loss: 4.5132 - val_accuracy: 0.7705 - val_loss: 3.6992

<keras.src.callbacks.history.History at 0x747bef08e590>

We quickly get to 80% validation accuracy.

Inference on new data

To get a prediction for a new sample, you can simply call model.predict(). There are just two things you need to do:

1. wrap scalars into a list so as to have a batch dimension (models only process batches of data, not single samples)
2. Call convert_to_tensor on each feature

`sample = { "age": 60, "sex": 1, "cp": 1, "trestbps": 145, "chol": 233, "fbs": 1, "restecg": 2, "thalach": 150, "exang": 0, "oldpeak": 2.3, "slope": 3, "ca": 0, "thal": "fixed", }

Given the category (in the sample above - key) and the category value (in the sample above - value),

we return its one-hot encoding

def get_cat_encoding(cat, cat_value): # Create a list of zeros with the same length as categories encoding = [0] * len(cat) # Find the index of category_value in categories and set the corresponding position to 1 if cat_value in cat: encoding[cat.index(cat_value)] = 1 return encoding

for name, value in sample.items(): if name in CATEGORICAL_FEATURES_WITH_VOCABULARY: sample.update( { name: get_cat_encoding( CATEGORICAL_FEATURES_WITH_VOCABULARY[name], sample[name] ) } )

Convert inputs to tensors

input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()} predictions = model.predict(input_dict)

print( f"This particular patient had a {100 * predictions[0][0]:.1f} " "percent probability of having a heart disease, " "as evaluated by our model." ) `

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step



1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step

This particular patient had a 0.0 percent probability of having a heart disease, as evaluated by our model.

Conclusions

• The orignal model (the one that runs only on tensorflow) converges quickly to around 80% and remains there for extended periods and at times hits 85%
• The updated model (the backed-agnostic) model may fluctuate between 78% and 83% and at times hitting 86% validation accuracy and converges around 80% also.