fixing Concat bug (03b0c2a3) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/unet.py

+11 −9

Original line number	Diff line number	Diff line
		@@ -113,14 +113,15 @@ class UNet(TensorGraph):
		in_layers=[conv5])

		up6 = Conv2DTranspose(
		num_outputs=self.filters[3], kernel_size=2, in_layers=[conv5])
		concat6 = Concat(in_layers=[conv4, up6], axis=1)
		num_outputs=self.filters[3], kernel_size=2, stride=2, in_layers=[conv5])
		concat6 = Concat(in_layers=[conv4, up6], axis=3)
		conv6 = Conv2D(
		num_outputs=self.filters[3],
		kernel_size=3,
		activation='relu',
		padding='same',
		in_layers=[concat6])

		conv6 = Conv2D(
		num_outputs=self.filters[3],
		kernel_size=3,
		@@ -129,8 +130,8 @@ class UNet(TensorGraph):
		in_layers=[conv6])

		up7 = Conv2DTranspose(
		num_outputs=self.filters[2], kernel_size=2, in_layers=[conv6])
		concat7 = Concat(in_layers=[conv3, up7], axis=1)
		num_outputs=self.filters[2], kernel_size=2, stride=2, in_layers=[conv6])
		concat7 = Concat(in_layers=[conv3, up7], axis=3)
		conv7 = Conv2D(
		num_outputs=self.filters[2],
		kernel_size=3,
		@@ -145,8 +146,8 @@ class UNet(TensorGraph):
		in_layers=[conv7])

		up8 = Conv2DTranspose(
		num_outputs=self.filters[1], kernel_size=2, in_layers=[conv7])
		concat8 = Concat(in_layers=[conv2, up8], axis=1)
		num_outputs=self.filters[1], kernel_size=2, stride=2, in_layers=[conv7])
		concat8 = Concat(in_layers=[conv2, up8], axis=3)
		conv8 = Conv2D(
		num_outputs=self.filters[1],
		kernel_size=3,
		@@ -161,8 +162,8 @@ class UNet(TensorGraph):
		in_layers=[conv8])

		up9 = Conv2DTranspose(
		num_outputs=self.filters[0], kernel_size=2, in_layers=[conv8])
		concat9 = Concat(in_layers=[conv1, up9], axis=1)
		num_outputs=self.filters[0], kernel_size=2, stride=2, in_layers=[conv8])
		concat9 = Concat(in_layers=[conv1, up9], axis=3)
		conv9 = Conv2D(
		num_outputs=self.filters[0],
		kernel_size=3,
		@@ -180,6 +181,7 @@ class UNet(TensorGraph):
		num_outputs=1, kernel_size=1, activation='sigmoid', in_layers=[conv9])

		loss = SoftMaxCrossEntropy(in_layers=[labels, conv10])
		loss = ReduceMean(in_layers=[loss])
		# loss = ReduceMean(in_layers=[loss])
		model.set_loss(loss)
		model.add_output(conv10)
		self.model = model

Admin message