Add patience, save figure

network.py

@@ -184,18 +184,18 @@ def evaluate(model, samples):
 TRAIN_DATA, TEST_DATA = dataset_get_sin()
 # TRAIN_DATA, TEST_DATA = dataset_get_linear()
 
-MODEL = Model(60, sigmoid, d_sigmoid, DATA_TYPE)
+MODEL = Model(6, sigmoid, d_sigmoid, DATA_TYPE)
 # MODEL = Model(10, relu, d_relu, DATA_TYPE)
 
 # Train the model with some training data
 TRAINING_ITERS = 5000
 LEARNING_RATE = 0.005
 TRAINING_SUBSET_SIZE = len(TRAIN_DATA)
+PATIENCE = 100
 
 print TRAINING_SUBSET_SIZE
 
 best_rate = np.inf
-rates = [["iter", "training_rate", "test_rate"]]
 for training_iter in range(TRAINING_ITERS):
     # Create a training sample
     training_subset_indices = npr.choice(
@@ -219,16 +219,21 @@ for training_iter in range(TRAINING_ITERS):
     # Evaluate accuracy against training data
     training_rate = evaluate(MODEL, training_subset)
     test_rate = evaluate(MODEL, TEST_DATA)
-    rates += [[training_iter, training_rate, test_rate]]
 
-    print training_iter, "positive rates:", training_rate, test_rate,
+    print training_iter, "cost:", training_rate, test_rate,
 
     # If it's the best one so far, store it
     if training_rate < best_rate:
         print "(new best)"
         best_rate = training_rate
+        patience = PATIENCE
     else:
-        print ""
+        patience -= 1
+        print patience
+
+    if patience <= 0:
+        print PATIENCE, "iterations without improvement"
+        break
 
 TEST_OUTPUT = np.vectorize(MODEL.f)(TEST_DATA[:, 0])
 TRAIN_OUTPUT = np.vectorize(MODEL.f)(TRAIN_DATA[:, 0])
@@ -240,5 +245,5 @@ scatter_train_out, = plt.plot(
 scatter_test_out, = plt.plot(
     TEST_DATA[:, 0], TEST_OUTPUT, 'bo', label="Network output on test data")
 plt.legend(handles=[scatter_train, scatter_train_out, scatter_test_out])
-
+plt.savefig("results.png", bbox_inches="tight")
 plt.show()