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blayer = BLayer.SpatialAveragePooling(
kw=1,
kh=self.klayer.pool_length,
dw=1,
dh=self.klayer.stride,
pad_w=bpadW,
pad_h=bpadH,
global_pooling=False,
ceil_mode=False,
count_include_pad=False,
divide=True,
format="NHWC",
bigdl_type="float"
)
seq.add(blayer)
seq.add(BLayer.Squeeze(3))
return seq
blayer = BLayer.LocallyConnected2D(n_input_plane=int(self.input_shape[2]),
input_width=1,
input_height=int(self.input_shape[1]),
n_output_plane=self.klayer.nb_filter,
kernel_w=1,
kernel_h=self.klayer.filter_length,
stride_w=1,
stride_h=self.klayer.subsample_length,
pad_w=0,
pad_h=0,
wRegularizer=to_bigdl_reg(self.config["W_regularizer"]),
bRegularizer=to_bigdl_reg(self.config["b_regularizer"]),
with_bias=self.klayer.bias,
data_format="NHWC")
seq.add(blayer)
seq.add(BLayer.Squeeze(3))
if self.config["activation"] != "linear":
activation = get_activation_by_name(self.config["activation"],
"%s_%s" % (self.config["name"], self.config["activation"]))
return self.fuse(seq, activation)
else:
return seq
seq = BLayer.Sequential()
blayer = BLayer.SpatialMaxPooling(
kw=b_kw,
kh=b_kh,
dw=b_kw,
dh=b_kh,
pad_w=0,
pad_h=0,
to_ceil=False,
format=bigdl_order,
bigdl_type="float"
)
seq.add(blayer)
if bigdl_order == "NCHW":
seq.add(BLayer.Squeeze(3, num_input_dims=3))
seq.add(BLayer.Squeeze(2, num_input_dims=2))
else:
seq.add(BLayer.Squeeze(2, num_input_dims=3))
seq.add(BLayer.Squeeze(1, num_input_dims=2))
return seq
pad_w=0,
pad_h=0,
global_pooling=False,
ceil_mode=False,
count_include_pad=False,
divide=True,
format=bigdl_order,
bigdl_type="float"
)
seq.add(blayer)
if bigdl_order == "NCHW":
seq.add(BLayer.Squeeze(3, num_input_dims=3))
seq.add(BLayer.Squeeze(2, num_input_dims=2))
else:
seq.add(BLayer.Squeeze(2, num_input_dims=3))
seq.add(BLayer.Squeeze(1, num_input_dims=2))
return seq
blayer = BLayer.SpatialAveragePooling(
kw=b_kw,
kh=b_kh,
dw=1,
dh=1,
pad_w=0,
pad_h=0,
global_pooling=False,
ceil_mode=False,
count_include_pad=False,
divide=True,
format="NHWC",
bigdl_type="float"
)
seq.add(blayer)
seq.add(BLayer.Squeeze(3))
seq.add(BLayer.Squeeze(2))
return seq
seq = BLayer.Sequential()
seq.add(BLayer.Reshape([int(self.input_shape[1]), 1, int(self.input_shape[2])], True))
blayer = BLayer.SpatialMaxPooling(
kw=1,
kh=self.klayer.pool_length,
dw=1,
dh=self.klayer.stride,
pad_w=bpadW,
pad_h=bpadH,
to_ceil=False,
format="NHWC",
bigdl_type="float"
)
seq.add(blayer)
seq.add(BLayer.Squeeze(3))
return seq
kw=b_kw,
kh=b_kh,
dw=b_kw,
dh=b_kh,
pad_w=0,
pad_h=0,
to_ceil=False,
format=bigdl_order,
bigdl_type="float"
)
seq.add(blayer)
if bigdl_order == "NCHW":
seq.add(BLayer.Squeeze(3, num_input_dims=3))
seq.add(BLayer.Squeeze(2, num_input_dims=2))
else:
seq.add(BLayer.Squeeze(2, num_input_dims=3))
seq.add(BLayer.Squeeze(1, num_input_dims=2))
return seq
seq = BLayer.Sequential()
seq.add(BLayer.Reshape([int(self.input_shape[1]), 1, int(self.input_shape[2])], True))
blayer = BLayer.SpatialMaxPooling(
kw=b_kw,
kh=b_kh,
dw=1,
dh=1,
pad_w=0,
pad_h=0,
to_ceil=False,
format="NHWC",
bigdl_type="float"
)
seq.add(blayer)
seq.add(BLayer.Squeeze(3))
seq.add(BLayer.Squeeze(2))
return seq
if hidden_size < 0:
raise TypeError('hidden_size must be greater than 0 with default embedding layer')
from bigdl.nn.layer import Squeeze
word_input = InputLayer(input_shape=(seq_len,))
postion_input = InputLayer(input_shape=(seq_len,))
embedding = Sequential()
embedding.add(Merge(layers=[word_input, postion_input], mode='concat')) \
.add(Reshape([seq_len * 2])) \
.add(Embedding(vocab, hidden_size, input_length=seq_len * 2,
weights=np.random.normal(0.0, initializer_range, (vocab, hidden_size))))\
.add(Dropout(embedding_drop)) \
.add(Reshape((seq_len, 2, hidden_size))) \
.add(KerasLayerWrapper(Sum(dimension=3, squeeze=True)))
# walk around for bug #1208, need remove this line after the bug fixed
embedding.add(KerasLayerWrapper(Squeeze(dim=3)))
shape = ((seq_len,), (seq_len,))
return TransformerLayer(n_block, hidden_drop, attn_drop, n_head, initializer_range,
bidirectional, output_all_block, embedding, input_shape=shape)