%tensorflow_version 2.x
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
url = 'https://storage.googleapis.com/applied-dl/heart.csv'
df = pd.read_csv(url)
df.head()
train, test = train_test_split(df, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
tf.data¶# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('target')
# WTF IS HAPPENING HERE
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
batch_size = 5 # A small batch sized is used for demonstration purposes
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
for feature_batch, label_batch in train_ds.take(1):
print('Every feature:', list(feature_batch.keys()))
print('A batch of ages:', feature_batch['age'])
print('A batch of targets:', label_batch )
# We will use this batch to demonstrate several types of feature columns
# A utility method to create a feature column
# and to transform a batch of data
example_batch = next(iter(train_ds))[0]
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
age = feature_column.numeric_column("age")
demo(age)
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
demo(age_buckets)
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
demo(thal_one_hot)
thal_embedding = feature_column.embedding_column(thal, dimension=8)
demo(thal_embedding)
thal_hashed = feature_column.categorical_column_with_hash_bucket(
'thal', hash_bucket_size=1000)
demo(feature_column.indicator_column(thal_hashed))
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
demo(feature_column.indicator_column(crossed_feature))
feature_columns = []
# numeric cols
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
feature_columns.append(feature_column.numeric_column(header))
# bucketized cols
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# indicator cols
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
feature_columns.append(thal_one_hot)
# embedding cols
thal_embedding = feature_column.embedding_column(thal, dimension=8)
feature_columns.append(thal_embedding)
# crossed cols
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
crossed_feature = feature_column.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)
After we've defined our feature columns, we will use a DenseFeatures layer to input them into our Keras model
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
batch_size = 32
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(1)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(train_ds,
validation_data=val_ds,
epochs=5)
loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)