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Add patience, save figure

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Carl Pearson
2016-11-16 17:44:12 -06:00
parent 4c54bfadd7
commit 578774561c
1 changed files with 11 additions and 6 deletions
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17
network.py
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@@ -184,18 +184,18 @@ def evaluate(model, samples):
TRAIN_DATA, TEST_DATA = dataset_get_sin() TRAIN_DATA, TEST_DATA = dataset_get_sin()
# TRAIN_DATA, TEST_DATA = dataset_get_linear() # 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) # MODEL = Model(10, relu, d_relu, DATA_TYPE)
# Train the model with some training data # Train the model with some training data
TRAINING_ITERS = 5000 TRAINING_ITERS = 5000
LEARNING_RATE = 0.005 LEARNING_RATE = 0.005
TRAINING_SUBSET_SIZE = len(TRAIN_DATA) TRAINING_SUBSET_SIZE = len(TRAIN_DATA)
PATIENCE = 100
print TRAINING_SUBSET_SIZE print TRAINING_SUBSET_SIZE
best_rate = np.inf best_rate = np.inf
rates = [["iter", "training_rate", "test_rate"]]
for training_iter in range(TRAINING_ITERS): for training_iter in range(TRAINING_ITERS):
# Create a training sample # Create a training sample
training_subset_indices = npr.choice( training_subset_indices = npr.choice(
@@ -219,16 +219,21 @@ for training_iter in range(TRAINING_ITERS):
# Evaluate accuracy against training data # Evaluate accuracy against training data
training_rate = evaluate(MODEL, training_subset) training_rate = evaluate(MODEL, training_subset)
test_rate = evaluate(MODEL, TEST_DATA) 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 it's the best one so far, store it
if training_rate < best_rate: if training_rate < best_rate:
print "(new best)" print "(new best)"
best_rate = training_rate best_rate = training_rate
patience = PATIENCE
else: else:
print "" patience -= 1
print patience
if patience <= 0:
print PATIENCE, "iterations without improvement"
break
TEST_OUTPUT = np.vectorize(MODEL.f)(TEST_DATA[:, 0]) TEST_OUTPUT = np.vectorize(MODEL.f)(TEST_DATA[:, 0])
TRAIN_OUTPUT = np.vectorize(MODEL.f)(TRAIN_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( scatter_test_out, = plt.plot(
TEST_DATA[:, 0], TEST_OUTPUT, 'bo', label="Network output on test data") TEST_DATA[:, 0], TEST_OUTPUT, 'bo', label="Network output on test data")
plt.legend(handles=[scatter_train, scatter_train_out, scatter_test_out]) plt.legend(handles=[scatter_train, scatter_train_out, scatter_test_out])
plt.savefig("results.png", bbox_inches="tight")
plt.show() plt.show()