hongthaiphi

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hongthaiphi

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hongthaiphi

import numpy as np

# sigmoid function
def nonlin(x, deriv=False):
    if(deriv==True):
        return x*(1-x)
    return 1/(1+np.exp(-x))

# input dataset
X = np.array([ [0,0,1],

hongthaiphi

X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
  l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
  l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
  l2_delta = (y - l2)*(l2*(1-l2))
  l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
  syn1 += l1.T.dot(l2_delta)

hongthaiphi

def dual_encoder_model(hparams, mode, context, context_len, utterance, utterance_len, targets):
    # Initialize embedidngs randomly or with pre-trained vectors if available
    embeddings_W = get_embeddings(hparams)
    # Embed the context and the utterance
    context_embedded = tf.nn.embedding_lookup(embeddings_W, context, name="embed_context")
    utterance_embedded = tf.nn.embedding_lookup(embeddings_W, utterance, name="embed_utterance")
    # Build the RNN
    with tf.variable_scope("rnn") as vs:
        # We use an LSTM Cell
        cell = tf.nn.rnn_cell.LSTMCell(hparams.rnn_dim, forget_bias=2.0, use_peepholes=True, state_is_tuple=True)

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estimator = tf.contrib.learn.Estimator(model_fn=model_fn, model_dir=MODEL_DIR, config=tf.contrib.learn.RunConfig())
input_fn_train = udc_inputs.create_input_fn(mode=tf.contrib.learn.ModeKeys.TRAIN, input_files=[TRAIN_FILE], batch_size=hparams.batch_size)
input_fn_eval = udc_inputs.create_input_fn(mode=tf.contrib.learn.ModeKeys.EVAL, input_files=[VALIDATION_FILE], batch_size=hparams.eval_batch_size, num_epochs=1)
eval_metrics = udc_metrics.create_evaluation_metrics()
# We need to subclass theis manually for now. The next TF version will
# have support ValidationMonitors with metrics built-in.
# It’s already on the master branch.
class EvaluationMonitor(tf.contrib.learn.monitors.EveryN):
    def every_n_step_end(self, step, outputs):
        self._estimator.evaluate(input_fn=input_fn_eval, metrics=eval_metrics, steps=None)

hongthaiphi

class TFIDFPredictor:
    def __init__(self):
        self.vectorizer = TfidfVectorizer()
    def train(self, data):
        self.vectorizer.fit(np.append(data.Context.values,data.Utterance.values))
    def predict(self, context, utterances):
        # Convert context and utterances into tfidf vector
        vector_context = self.vectorizer.transform([context])
        vector_doc = self.vectorizer.transform(utterances)
        # The dot product measures the similarity of the resulting vectors

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# Random Predictor
def predict_random(context, utterances):
    return np.random.choice(len(utterances), 10, replace=False)
# Evaluate Random predictor
    y_random = [predict_random(test_df.Context[x], test_df.iloc[x,1:].values) for x in range(len(test_df))]
    y_test = np.zeros(len(y_random))
    for n in [1, 2, 5, 10]:
        print("Recall @ ({}, 10): {:g}".format(n, evaluate_recall(y_random, y_test, n)))

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def evaluate_recall(y, y_test, k=1):
    num_examples = float(len(y))
    num_correct = 0
    for predictions, label in zip(y, y_test):
        if label in predictions[:k]:
            num_correct += 1
    return num_correct/num_examples