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Regression
The Estimator outputs the log probabilities (logits), the softmax probabilities for the
ten classes, and the corresponding label:
Let us build our model.
1. The first step is as always importing the modules needed:
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)
2. We take the input data of MNIST from the tensorflow.keras dataset:
# Load training and eval data
((train_data, train_labels),
(eval_data, eval_labels)) = tf.keras.datasets.mnist.load_data()
3. Next, we preprocess the data:
train_data = train_data/np.float32(255)
train_labels = train_labels.astype(np.int32)
eval_data = eval_data/np.float32(255)
eval_labels = eval_labels.astype(np.int32)
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