def alexnet(images, trainable=True):
conv1 = conv2d_layer("conv1",
images,
filters=64,
kernel_size=11,
strides=4,
padding="VALID")
pool1 = flow.nn.avg_pool2d(conv1, 3, 2, "VALID", "NCHW", name="pool1")
conv2 = conv2d_layer("conv2", pool1, filters=192, kernel_size=5)
pool2 = flow.nn.avg_pool2d(conv2, 3, 2, "VALID", "NCHW", name="pool2")
conv3 = conv2d_layer("conv3", pool2, filters=384)
conv4 = conv2d_layer("conv4", conv3, filters=384)
conv5 = conv2d_layer("conv5", conv4, filters=256)
pool5 = flow.nn.avg_pool2d(conv5, 3, 2, "VALID", "NCHW", name="pool5")
if len(pool5.shape) > 2:
pool5 = flow.reshape(pool5, shape=(pool5.shape[0], -1))
fc1 = flow.layers.dense(
inputs=pool5,
units=4096,
activation=flow.math.relu,
use_bias=False,
kernel_initializer=flow.random_uniform_initializer(),
bias_initializer=False,
trainable=trainable,
name="fc1",
)
dropout1 = flow.nn.dropout(fc1, rate=0.5)
fc2 = flow.layers.dense(
inputs=dropout1,
units=4096,
activation=flow.math.relu,
use_bias=False,
kernel_initializer=flow.random_uniform_initializer(),
bias_initializer=False,
trainable=trainable,
name="fc2",
)
dropout2 = flow.nn.dropout(fc2, rate=0.5)
fc3 = flow.layers.dense(
inputs=dropout2,
units=1001,
activation=None,
use_bias=False,
kernel_initializer=flow.random_uniform_initializer(),
bias_initializer=False,
trainable=trainable,
name="fc3",
)
return fc3
def _conv_block(in_blob, index, filters, conv_times):
conv_block = []
conv_block.insert(0, in_blob)
for i in range(conv_times):
conv_i = conv2d_layer(
name="conv{}".format(index),
input=conv_block[i],
filters=filters,
kernel_size=3,
strides=1,
)
conv_block.append(conv_i)
index += 1
return conv_block