def evaluate(self, embedding, train_data, validation_data, test_data, num_classes):
"""
Evaluates the 'embedding' using a convolutional neural network for NLP (from Yoon Kim [2014]) on 'dataset'
Parameters
----------
embedding : An embedding which implements the Embedding interface
train_data ; A tuple of lists (docs, y) that constitutes the training data
validation_data: A tuple of lists (docs, y) that constitutes the validation data
Returns : A float, with the top accuracy achieved
-------
"""
# Load dataset
train_set = zip(*train_data)
validation_set = zip(*validation_data)
test_set = zip(*test_data)
longest_doc = 0
for (doc, y) in train_set + validation_set + test_set:
l = len(doc.split(" "))
if l > longest_doc:
longest_doc = l
# Train CNN
perf = conv_net_sentence.train_conv_net(
datasets=(train_set, validation_set, test_set),
embedding=embedding,
longest_doc=longest_doc,
lr_decay=0.95,
filter_hs=[3, 4, 5],
conv_non_linear="relu",
hidden_units=[100, 2],
shuffle_batch=True,
n_epochs=self.n_epochs,
sqr_norm_lim=9,
non_static=False,
batch_size=self.batch_size,
dropout_rate=[0.5],
)
print "perf: " + str(perf)
return perf
def RoB_CNN_theano(maxlen=2000):
'''
Process data for CNN classification via the
theano implementation (Kim, 2014)
'''
###
# read in data (this also fits a vectorizer for us)
train_docs, y_train, vectorizer = read_RoB_data(
path="train-Xy-Random-sequence-generation.txt",
y_tuples=False,
max_features=50000)
vocab_size = len(vectorizer.vocabulary_)
print("vocabulary size of training data: " + str(vocab_size))
test_docs, y_test = read_RoB_data(
path="test-Xy-Random-sequence-generation.txt",
fit_vectorizer=False,
y_tuples=False)
y_train = np.array(y_train)
y_test = np.array(y_test)
# map to word indices
X_train = to_token_indices(train_docs,
vectorizer) ##convert raw texts to word indices
X_test = to_token_indices(test_docs, vectorizer)
# read in pretrained word vectors
wv = load_trained_w2v_model()
wv_dim = wv.vector_size
print("dimension of word embedding: " + str(wv_dim))
# set initial word vectors for all token indices
init_vectors, unk_vecs = _get_init_vectors(vectorizer, wv)
W = init_vectors.astype(theano.config.floatX)
W = np.vstack((np.zeros(wv_dim),
W)) #####add zeros vectors as the 0th word embedding
W = W.astype(theano.config.floatX)
print("dimension of W matrix is: " +
str(W.shape)) #####should be 50001 times 200
# zero-pad sentences
## @TMP only one set of filters
#filter_heights = [3,4,5]
filter_heights = [3, 4, 5]
pad_len = maxlen + 2 * (max(filter_heights) - 1)
X_train = pad_sequences(X_train,
maxlen=maxlen,
padding="post",
dtype=np.int32) ##X_train is indices
X_test = pad_sequences(X_test,
maxlen=maxlen,
padding="post",
dtype=np.int32)
print('X_train shape: ', X_train.shape)
print('X_test shape: ', X_test.shape)
'''
Kim's code expects datasets to contain train and test
matrices, *with the labels as the last entries!*
'''
X_y_train = np.array(np.hstack((X_train, np.matrix(y_train).T)))
X_y_test = np.array(np.hstack((X_test, np.matrix(y_test).T)))
datasets = [X_y_train, X_y_test]
# @TMP setting these to small values
n_filters = 100 # number of feature maps per height
batch_size = 50
n_epochs = 20
perf = conv_net_sentence.train_conv_net(datasets,
W,
img_w=W.shape[1],
lr_decay=0.95,
filter_hs=filter_heights,
conv_non_linear="relu",
hidden_units=[n_filters, 2],
shuffle_batch=True,
n_epochs=n_epochs,
sqr_norm_lim=9,
non_static=True,
batch_size=batch_size,
dropout_rate=[0.5])
return perf
def RoB_CNN_theano(maxlen=4000):
'''
Process data for CNN classification via the
theano implementation (Kim, 2014)
'''
###
# read in data (this also fits a vectorizer for us)
train_docs, y_train, vectorizer = read_RoB_data(path="train-Xy-Random-sequence-generation.txt",
y_tuples=False, max_features=50000)
vocab_size = len(vectorizer.vocabulary_)
test_docs, y_test = read_RoB_data(path="test-Xy-Random-sequence-generation.txt",
fit_vectorizer=False,
y_tuples=False)
y_train = np.array(y_train)
y_test = np.array(y_test)
# map to word indices
X_train = to_token_indices(train_docs, vectorizer)
X_test = to_token_indices(test_docs, vectorizer)
# read in pretrained word vectors
wv = load_trained_w2v_model()
wv_dim = wv.vector_size
# set initial word vectors for all token indices
init_vectors, unk_vecs = _get_init_vectors(vectorizer, wv)
W = init_vectors.astype(np.float32)
# zero-pad sentences
## @TMP only one set of filters
#filter_heights = [3,4,5]
filter_heights = [9,10,11]
pad_len = maxlen + 2*(max(filter_heights)-1)
X_train = pad_sequences(X_train, maxlen=pad_len, padding="post", dtype=np.int32)
X_test = pad_sequences(X_test, maxlen=pad_len, padding="post", dtype=np.int32)
print('X_train shape: ', X_train.shape)
'''
Kim's code expects datasets to contain train and test
matrices, *with the labels as the last entries!*
'''
X_y_train = np.array(np.hstack((X_train, np.matrix(y_train).T)))
X_y_test = np.array(np.hstack((X_test, np.matrix(y_test).T)))
datasets = [X_y_train, X_y_test]
# @TMP setting these to small values
n_filters = 100 # number of feature maps per height
batch_size = 25
n_epochs = 10
perf = conv_net_sentence.train_conv_net(datasets,
W,
img_w = W.shape[1],
lr_decay=0.95,
filter_hs=filter_heights,
conv_non_linear="relu",
hidden_units=[n_filters,2],
shuffle_batch=True,
n_epochs=n_epochs,
sqr_norm_lim=9,
non_static=True,
batch_size=batch_size,
dropout_rate=[0.25])
return perf
