print("############################")
print("# Test FORWARD PROPAGATION #")
print("############################")
model_test_forward_prop = RNNNumpy(vocabulary_size)
# Using 10th training example
o, s = model_test_forward_prop.forward_propagation(X_train[10])
print(o.shape) # (45, 8000)
print(o)
# Try calculating a prediction by forward-propagating
# with the current weight values
# even though they obviously would be very far from optimal
predictions = model_test_forward_prop.predict(X_train[10])
print(predictions.shape)
print(predictions)
# According to the tutorial: Since we have (vocabulary_size) words, so each word
# should be predicted, on average, with probability 1/C, which would yield
# a loss of L = -1/N * N * log(1/C) = log(C)
print("Expected loss for random predictions: %f" % np.log(vocabulary_size))
print("Actual loss: %f" %
model_test_forward.calculate_loss(X_train[:1000], y_train[:1000]))
print()
print("#######################")
print("# Test GRADIENT CHECK #")
print("#######################")
import preprocess from rnn_numpy import RNNNumpy import numpy as np X_train,y_train,vocabulary_size = preprocess.create_train_data() np.random.seed(10) model = RNNNumpy(vocabulary_size) output, hidden_states = model.forward_propagation(X_train[10]) print output.shape print output predictions = model.predict(X_train[10]) print predictions.shape print predictions # Limit to 1000 examples to save time print "Expected Loss for random predictions: %f" % np.log(vocabulary_size) print "Actual loss: %f" % model.calculate_loss(X_train[:1000], y_train[:1000])
