def conv_block(input, conv_dim, input_dim, res_params, scope):
with tf.variable_scope(scope):
input_conv = tf.reshape(input, [-1, 1, input_dim, 1])
single_conv = slim.conv2d(activation_fn=None,
inputs=input_conv,
num_outputs=conv_dim,
kernel_size=[1, 1],
stride=[1, 4],
padding='SAME')
pair_conv = slim.conv2d(activation_fn=None,
inputs=input_conv,
num_outputs=conv_dim,
kernel_size=[1, 2],
stride=[1, 4],
padding='SAME')
triple_conv = slim.conv2d(activation_fn=None,
inputs=input_conv,
num_outputs=conv_dim,
kernel_size=[1, 3],
stride=[1, 4],
padding='SAME')
quadric_conv = slim.conv2d(activation_fn=None,
inputs=input_conv,
num_outputs=conv_dim,
kernel_size=[1, 4],
stride=[1, 4],
padding='SAME')
conv_list = [single_conv, pair_conv, triple_conv, quadric_conv]
conv = tf.concat(conv_list, -1)
# conv_idens = []
# for c in conv_list:
# for i in range(5):
# c = identity_block(c, 32, 3)
# conv_idens.append(c)
# conv = tf.concat(conv_idens, -1)
for param in res_params:
if param[-1] == 'identity':
conv = identity_block(conv, param[0], param[1])
elif param[-1] == 'downsampling':
conv = downsample_block(conv, param[0], param[1])
elif param[-1] == 'upsampling':
conv = upsample_block(conv, param[0], param[1])
else:
raise Exception('unsupported layer type')
# assert conv.shape[1] * conv.shape[2] * conv.shape[3] == 1024
conv = tf.reshape(conv,
[-1, conv.shape[1] * conv.shape[2] * conv.shape[3]])
# conv = tf.squeeze(tf.reduce_mean(conv, axis=[2]), axis=[1])
return conv
def build_graph(self, onehot_cards):
scope = 'AutoEncoder'
with tf.variable_scope(scope):
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
weights_regularizer=slim.l2_regularizer(1e-3)):
input_conv = tf.reshape(onehot_cards, [-1, 1, INPUT_DIM, 1])
single_conv = slim.conv2d(activation_fn=None, inputs=input_conv, num_outputs=32,
kernel_size=[1, 1], stride=[1, 4], padding='SAME')
pair_conv = slim.conv2d(activation_fn=None, inputs=input_conv, num_outputs=32,
kernel_size=[1, 2], stride=[1, 4], padding='SAME')
triple_conv = slim.conv2d(activation_fn=None, inputs=input_conv, num_outputs=32,
kernel_size=[1, 3], stride=[1, 4], padding='SAME')
quadric_conv = slim.conv2d(activation_fn=None, inputs=input_conv, num_outputs=32,
kernel_size=[1, 4], stride=[1, 4], padding='SAME')
conv = tf.concat([single_conv, pair_conv, triple_conv, quadric_conv], -1)
encoding_params = [[128, 3, 'identity'],
[128, 3, 'identity'],
[128, 3, 'downsampling'],
[128, 3, 'identity'],
[128, 3, 'identity'],
[256, 3, 'downsampling'],
[256, 3, 'identity'],
[256, 3, 'identity']
]
for param in encoding_params:
if param[-1] == 'identity':
conv = identity_block(conv, param[0], param[1])
elif param[-1] == 'upsampling':
conv = upsample_block(conv, param[0], param[1])
elif param[-1] == 'downsampling':
conv = downsample_block(conv, param[0], param[1])
else:
raise Exception('unsupported layer type')
conv = tf.reduce_mean(conv, [1, 2], True)
encoding = tf.identity(conv, name='encoding')
# is_training = get_current_tower_context().is_training
# if not is_training:
# return
decoding_params = [[256, 4, 'upsampling'],
[256, 3, 'identity'],
[256, 3, 'identity'],
[256, 4, 'upsampling'],
[128, 3, 'identity'],
[128, 3, 'identity'],
[128, 4, 'upsampling'],
[128, 3, 'identity'],
[1, 3, 'identity']
]
for param in decoding_params:
if param[-1] == 'identity':
conv = identity_block(conv, param[0], param[1])
elif param[-1] == 'upsampling':
conv = upsample_block(conv, param[0], param[1])
elif param[-1] == 'downsampling':
conv = downsample_block(conv, param[0], param[1])
else:
raise Exception('unsupported layer type')
print(conv.shape)
decoded = tf.reshape(conv, [-1, conv.shape[1] * conv.shape[2] * conv.shape[3]])
reconstuct_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.pad(onehot_cards, [[0, 0], [0, 4]]), logits=decoded)
reconstuct_loss = tf.reduce_mean(tf.reduce_sum(reconstuct_loss, -1), name='reconstruct_loss')
l2_loss = tf.truediv(regularize_cost_from_collection(), tf.cast(tf.shape(onehot_cards)[0], tf.float32), name='l2_loss')
add_moving_summary(reconstuct_loss, decay=0)
add_moving_summary(l2_loss, decay=0)
loss = reconstuct_loss + l2_loss
return loss