added project structure. believe there is a better way to do this.

run.py

@@ -0,0 +1,8 @@
+#!/usr/bin/env python3
+
+import pickle
+
+
+weights = pickle.load(open("weights.pkl", "rb"))
+
+

train.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+
+from sklearn.datasets import load_digits
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+import pickle
+
+digits = load_digits()
+
+# input
+x = digits.images.reshape((len(digits.images), -1))
+
+# what the output should be
+y = digits.target
+
+mlp = MLPClassifier(hidden_layer_sizes=(18,),
+                    activation='logistic',
+                    alpha=1e-4, solver='sgd',
+                    tol=1e-4, random_state=1,
+                    learning_rate_init=.1,
+                    verbose=True)
+
+mlp.fit(x, y)
+
+fig, axes = plt.subplots(1, 1)
+axes.plot(mlp.loss_curve_, 'o-')
+axes.set_xlabel("iterations")
+axes.set_ylabel("loss")
+plt.show()
+
+pickle.dump(mlp.coefs_, open( 'weights.pkl', 'wb'))