Here’s what it came up with:
import numpy as np
import tensorflow as tf# Define the model architecture
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(128, activation=’relu’, input_shape=(784,)))
model.add(tf.keras.layers.Dense(64, activation=’relu’))
model.add(tf.keras.layers.Dense(10, activation=’softmax’))# Compile the model
model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])# Load the data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype(‘float32’) / 255
x_test = x_test.astype(‘float32’) / 255# Convert the target vectors to categorical
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
# Train the model
model.fit(x_train, y_train, epochs=5, batch_size=128, validation_data=(x_test, y_test))
This code creates a neural network using the TensorFlow library. The network consists of three dense layers, with 128, 64, and 10 neurons respectively. The adam optimizer is used to train the network, and the categorical_crossentropy loss function is used to evaluate the performance of the network. The MNIST dataset, a widely used dataset for image recognition, is loaded and preprocessed, and the network is trained on the training data for 5 epochs with a batch size of 128.
