def test_SelectedMaskSoftmax():
X_ph = tf.placeholder('float32', [None, 20])
mask_ph = tf.placeholder('float32', [20])
X_sn = tg.StartNode(input_vars=[X_ph])
mask_sn = tg.StartNode(input_vars=[mask_ph])
merge_hn = tg.HiddenNode(prev=[X_sn, mask_sn],
input_merge_mode=SelectedMaskSoftmax())
y_en = tg.EndNode(prev=[merge_hn])
graph = tg.Graph(start=[X_sn, mask_sn], end=[y_en])
y_sb, = graph.train_fprop()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
mask_arr = np.zeros(20)
mask_arr[[2, 3, 4]] = 1
# import pdb; pdb.set_trace()
feed_dict = {X_ph: np.random.rand(3, 20), mask_ph: mask_arr}
out = sess.run(y_sb, feed_dict=feed_dict)
assert (out.sum(1) == 1).any()
print(out)
print('test passed!')
def test_SequenceMask():
X_ph = tf.placeholder('float32', [None, 5, 6, 7])
seq_ph = tf.placeholder('int32', [None])
X_sn = tg.StartNode(input_vars=[X_ph])
seq_sn = tg.StartNode(input_vars=[seq_ph])
merge_hn = tg.HiddenNode(prev=[X_sn, seq_sn],
input_merge_mode=SequenceMask(maxlen=5))
out_en = tg.EndNode(prev=[merge_hn])
graph = tg.Graph(start=[X_sn, seq_sn], end=[out_en])
y_train_sb = graph.train_fprop()
y_test_sb = graph.test_fprop()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
feed_dict = {X_ph: np.random.rand(3, 5, 6, 7), seq_ph: [2, 3, 4]}
y_train = sess.run(y_train_sb, feed_dict=feed_dict)[0]
y_test = sess.run(y_test_sb, feed_dict=feed_dict)[0]
assert y_train.sum() == y_test.sum()
assert y_train[0, :2].sum() > 0 and y_train[0, 2:].sum() == 0
assert y_train[1, :3].sum() > 0 and y_train[1, 3:].sum() == 0
assert y_train[2, :4].sum() > 0 and y_train[2, 4:].sum() == 0
print('test passed!')
def test_MaskSoftmax():
X_ph = tf.placeholder('float32', [None, 20])
seq_ph = tf.placeholder('int32', [None])
X_sn = tg.StartNode(input_vars=[X_ph])
seq_sn = tg.StartNode(input_vars=[seq_ph])
merge_hn = tg.HiddenNode(prev=[X_sn, seq_sn],
input_merge_mode=MaskSoftmax())
y_en = tg.EndNode(prev=[merge_hn])
graph = tg.Graph(start=[X_sn, seq_sn], end=[y_en])
y_sb, = graph.train_fprop()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
feed_dict = {X_ph: np.random.rand(3, 20), seq_ph: [5, 8, 0]}
out = sess.run(y_sb, feed_dict=feed_dict)
assert (out[0][5:].sum() - 0)**2 < 1e-6
assert (out[0][:5].sum() - 1)**2 < 1e-6
assert (out[1][8:].sum() - 0)**2 < 1e-6
assert (out[1][:8].sum() - 1)**2 < 1e-6
assert (out[2].sum() - 0)**2 < 1e-6
print('test passed!')
def __init__(self, h, w, c, nclass):
layers = []
layers.append(CBR(h,w,c))
layers.append(Flatten())
layers.append(Linear(1*h*w, nclass))
self.startnode = tg.StartNode(input_vars=[None])
hn = tg.HiddenNode(prev=[self.startnode], layers=layers)
self.endnode = tg.EndNode(prev=[hn])
def model(word_len, sent_len, nclass):
unicode_size = 1000
ch_embed_dim = 20
h, w = valid(ch_embed_dim,
word_len,
stride=(1, 1),
kernel_size=(ch_embed_dim, 5))
h, w = valid(h, w, stride=(1, 1), kernel_size=(1, 5))
h, w = valid(h, w, stride=(1, 2), kernel_size=(1, 5))
conv_out_dim = int(h * w * 60)
X_ph = tf.placeholder('int32', [None, sent_len, word_len])
input_sn = tg.StartNode(input_vars=[X_ph])
charcnn_hn = tg.HiddenNode(prev=[input_sn],
layers=[
Reshape(shape=(-1, word_len)),
Embedding(cat_dim=unicode_size,
encode_dim=ch_embed_dim,
zero_pad=True),
Reshape(shape=(-1, ch_embed_dim, word_len,
1)),
Conv2D(input_channels=1,
num_filters=20,
padding='VALID',
kernel_size=(ch_embed_dim, 5),
stride=(1, 1)),
RELU(),
Conv2D(input_channels=20,
num_filters=40,
padding='VALID',
kernel_size=(1, 5),
stride=(1, 1)),
RELU(),
Conv2D(input_channels=40,
num_filters=60,
padding='VALID',
kernel_size=(1, 5),
stride=(1, 2)),
RELU(),
Flatten(),
Linear(conv_out_dim, nclass),
Reshape((-1, sent_len, nclass)),
ReduceSum(1),
Softmax()
])
output_en = tg.EndNode(prev=[charcnn_hn])
graph = tg.Graph(start=[input_sn], end=[output_en])
y_train_sb = graph.train_fprop()[0]
y_test_sb = graph.test_fprop()[0]
return X_ph, y_train_sb, y_test_sb
def __init__(self, h, w, c):
layers1 = []
layers1.append(Conv2D(input_channels=c, num_filters=1, kernel_size=(2,2), stride=(1,1), padding='SAME'))
layers1.append(BatchNormalization(input_shape=[h,w,1]))
layers1.append(RELU())
layers2 = []
layers2.append(Conv2D(input_channels=c, num_filters=1, kernel_size=(2,2), stride=(1,1), padding='SAME'))
layers2.append(BatchNormalization(input_shape=[h,w,1]))
layers2.append(RELU())
self.startnode = tg.StartNode(input_vars=[None])
hn1 = tg.HiddenNode(prev=[self.startnode], layers=layers1)
hn2 = tg.HiddenNode(prev=[self.startnode], layers=layers2)
hn3 = tg.HiddenNode(prev=[hn1, hn2], input_merge_mode=Sum())
self.endnode = tg.EndNode(prev=[hn3])