sklearn_simple_nn.py

import warnings

from sklearn.datasets import fetch_openml
from sklearn.exceptions import ConvergenceWarning
from sklearn.neural_network import MLPClassifier

# load MNIST data from fetch_openml
X, y = fetch_openml("mnist_784", version=1, return_X_y=True)
X = X/255.0

# get train/test split
X_train, X_test = X[:60000], X[60000:]
y_train, y_test = y[:60000], y[60000:]

# set up MLP Classifier
mlp = MLPClassifier(
    hidden_layer_sizes=(50,),
    max_iter=15,
    alpha=1e-4,
    solver="sgd",
    verbose=True,
    random_state=1,
    learning_rate_init=0.1
)

# We probably won't converge so we'll catch the warnings
with warnings.catch_warnings():
    warnings.filterwarnings("ignore", category=ConvergenceWarning, module="sklearn")
    mlp.fit(X_train, y_train)

# print out the model scores
print(f"Training set score: {mlp.score(X_train, y_train)}")
print(f"Test set score: {mlp.score(X_test, y_test)}")