def stateless_fit(model, X, y, Xtest, ytest, params):
"""
Train the model passed as 1st argument, and return the train_loss
X and Y Training values are passed.
Parameters dictionary is also necessary.
"""
for i in range(params['lstm_num_epochs']):
model.fit(
X, y,
epochs=1,
validation_data=(Xtest, ytest),
verbose=params['keras_verbose_level'],
shuffle=False,
batch_size=params['lstm_batch_size'])
model.reset_states()
return model
def range_predict(model, X_test, Y_test, params, batch_size=1):
"""
Make a prediction for a range of input values, by saturating the lstm
returns the predictions (unscaled) and the number of errors
"""
input_shape = (1, params['lstm_timesteps'], len(params['columNames']))
preds = zeros(X_test.shape[0])
for i in range(0, X_test.shape[0]):
input_vector = X_test[i].reshape(input_shape)
# Make a prediction, saturating
for k in range(0, params['num_saturations']):
y_hat = model.predict(input_vector, batch_size=batch_size)
model.reset_states()
preds[i] = y_hat
rmse, num_errors = compute.error(Y_test, preds)
return (preds, rmse, num_errors)
def generate_text(model, start_sequence, num_generate):
input_eval = [char2idx[s] for x in start_sequence]
input_eval = tf.expand_dims(input_eval, 0)
text_generated = []
temperature = 0.6
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
predictions = tf.squeeze(predictions, 0)
predictions = prediction / temperature
predicted_id = tf.random.categorical(predictions,
num_samples=1)[-1, 0].numpy()
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(int2char[predicted_id])
return text_generated
def generate_text(model, char2idx, start_string):
# Evaluation step (generating text using the learned model)
# Number of characters to generate
num_generate = 1000
# Converting our start string to numbers (vectorizing)
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
# Empty string to store our results
text_generated = start_string
# Low temperatures results in more predictable text.
# Higher temperatures results in more surprising text.
# Experiment to find the best setting.
temperature = 1.0
# Here batch size == 1
model.reset_states()
tf.random.set_seed(42)
for i in range(num_generate):
# if i > 200:
# print(str(i) + text_generated[-1])
predictions = model(input_eval)
# remove the batch
predictions = tf.squeeze(predictions, 0)
# using a categorical distribution to predict the character returned by the
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions[:, 1:],
num_samples=1)[-1, 0].numpy() + 1
# We pass the predicted character as the next input to the model
# along with the previous hidden state
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
return text_generated