Python Evaluation

Yrstruly2021 — Sat, 17 Jun 2023 19:07:23 GMT

Can anybody with Python experience test my code and let's compare answers/notes, please?

import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

abalone_train = pd.read_csv(
r"C:\Users\Anthony.DESKTOP-ES5HL78\Downloads\abalone.data",
sep=“,”,
names=[
“Sex”, “Length”, “Diameter”, “Height”, “Whole weight”,
“Shucked weight”, “Viscera weight”, “Shell weight”, “Age”
]
)

# Perform one-hot encoding for the ‘Sex’ column

abalone_train = pd.get_dummies(abalone_train, columns=[‘Sex’])

abalone_features = abalone_train.copy()
abalone_labels = abalone_features.pop(‘Age’)

# Convert to numpy array and float32 data type

abalone_features = abalone_features.astype(‘float32’)
abalone_labels = abalone_labels.astype(‘float32’)

# Create a sequential model

model = tf.keras.Sequential([
layers.Dense(64, activation=‘relu’),
layers.Dense(1)
])

# Compile the model

model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.MeanSquaredError())

# Fit the model

history = model.fit(abalone_features, abalone_labels, epochs=10, verbose=0)

# Print history and model summary

print(history.history)
model.summary()

{ [74.98324584960938, 12.322690963745117, 7.765621185302734, 7.502653121948242, 7.229104518890381, 6.997042179107666, 6.799757957458496, 6.645227432250977, 6.521533489227295, 6.418800354003906]}
Model: “sequential”

---

# Layer (type) Output Shape Param #

dense (Dense) (None, 64) 704

dense_1 (Dense) (None, 1) 65

=================================================================
Total params: 769
Trainable params: 769
Non-trainable params: 0

## Data for this Question: [https://drive.google.com/file/d/1iSgWZUK…sp=sharing](https://drive.google.com/file/d/1iSgWZUK23Gw6R-2rUkKeEOl6BIwms8L8/view?usp=sharing)

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Python Evaluation