
import os

import tensorflow as tf

# Create the model
def create_keras_model():
        
    model = tf.keras.Sequential(
        [
            tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(64, activation="relu"),
            tf.keras.layers.Dense(10, activation="softmax"),
        ]
    )

    model.compile(
        loss="sparse_categorical_crossentropy",
        optimizer=tf.keras.optimizers.Adam(),
        metrics=["accuracy"],
    )

    return model


import os

import tensorflow as tf

def create_keras_model():
        
    model = tf.keras.Sequential(
        [
            tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(64, activation="relu"),
            tf.keras.layers.Dense(10, activation="softmax"),
        ]
    )

    model.compile(
        loss="sparse_categorical_crossentropy",
        optimizer=tf.keras.optimizers.Adam(),
        metrics=["sparse_categorical_accuracy"],
    )

    return model

Format code using black and remove comments
Format code using black and remove comments
  
  import os
  
  import tensorflow as tf
  
- # Create the model
  def create_keras_model():
          
      model = tf.keras.Sequential(
          [
              tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
              tf.keras.layers.MaxPooling2D(),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(64, activation="relu"),
              tf.keras.layers.Dense(10, activation="softmax"),
          ]
      )
  
      model.compile(
          loss="sparse_categorical_crossentropy",
          optimizer=tf.keras.optimizers.Adam(),
-         metrics=["accuracy"],
+         metrics=["sparse_categorical_accuracy"],
      )
  
      return model
  
Format code using black and remove comments
## Code Before:

import os

import tensorflow as tf

# Create the model
def create_keras_model():
        
    model = tf.keras.Sequential(
        [
            tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(64, activation="relu"),
            tf.keras.layers.Dense(10, activation="softmax"),
        ]
    )

    model.compile(
        loss="sparse_categorical_crossentropy",
        optimizer=tf.keras.optimizers.Adam(),
        metrics=["accuracy"],
    )

    return model

## Instruction:
Format code using black and remove comments
## Code After:

import os

import tensorflow as tf

def create_keras_model():
        
    model = tf.keras.Sequential(
        [
            tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(64, activation="relu"),
            tf.keras.layers.Dense(10, activation="softmax"),
        ]
    )

    model.compile(
        loss="sparse_categorical_crossentropy",
        optimizer=tf.keras.optimizers.Adam(),
        metrics=["sparse_categorical_accuracy"],
    )

    return model
