conv block (af27ad55) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/resnet50.py

+50 −2

Original line number	Diff line number	Diff line
		@@ -10,7 +10,7 @@ import tensorflow as tf
		import deepchem as dc
		from deepchem.models import Sequential
		from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature, Label, BatchNorm
		from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy, ReduceMean, SoftMax
		from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy, ReduceMean, SoftMax, ReLU, Add
		from deepchem.models import TensorGraph


		@@ -29,6 +29,51 @@ class ResNet50(TensorGraph):
		specifies number of classes
		"""

		def conv_block(input, kernel_size, filters, strides=2):
		filters1, filters2, filters3 = filters
		output = Conv2D(
		num_outputs=filters1,
		kernel_size=1,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[input])
		output = BatchNorm(in_layers=[output])
		output = ReLU(output)

		output = Conv2D(
		num_outputs=filters2,
		kernel_size=kernel_size,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[output])
		output = BatchNorm(in_layers=[output])
		output = ReLU(output)

		output = Conv2D(
		num_outputs=filters3,
		kernel_size=1,
		stride=2,
		activation='linear',
		padding='same',
		in_layers=[output])
		output = BatchNorm(in_layers=[output])

		shortcut = Conv2D(
		num_outputs=filters3,
		kernel_size=1,
		stride=2,
		activation='linear',
		padding='same',
		in_layers=[input])
		shortcut = BatchNorm(in_layers=[shortcut])

		output = Add(in_layers[shortcut, output])
		output = ReLU(output)

		return output

		def __init__(self,
		img_rows=224,
		img_cols=224,
		@@ -47,8 +92,11 @@ class ResNet50(TensorGraph):
		num_outputs=64,
		kernel_size=7,
		stride=2,
		activation='relu',
		activation='linear',
		padding='same',
		in_layers=[input])
		bn1 = BatchNorm(in_layers=[conv1])
		ac1 = ReLU(bn1)
		pool1 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[bn1])

		cb1 = conv_block(pool1, 3, [64, 64, 256], 1)

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