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# hongthaiphi

Created Oct 27, 2018
View remove-footer.html
Created Oct 27, 2018
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Created Oct 27, 2018
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 .fixed-menu { position:fixed; top:0; padding:30px 0px 0px 0px; z-index: 99999; width: 100%; display:block; }
Created Oct 17, 2016
View 2layerNN.py
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 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],
Created Oct 17, 2016
View dl11.py
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 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)
Created Sep 21, 2016
View medium5.py
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 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)
Created Sep 21, 2016
View medium4.py
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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)
Created Sep 21, 2016
View medium3.py
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 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
Created Sep 21, 2016
View random_predictor.py
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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)))
Created Sep 21, 2016
View medium1.py
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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