def conv2d_input(x, filter=32, format="NHWC", name="conv_input", batch_normalization=True,
layer_list=None):
"""
:param x:
:param filter:
:param format:
:param name:
:param batch_normalization:
:param layer_list:
:return:
"""
with tf.name_scope(name) as scope:
x = tf.convert_to_tensor(x)
input_shape = x.get_shape()
N, H, W, C = (0, 0, 0, 0)
if format == "NHWC":
N, H, W, C = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
elif format == "NCHW":
N, C, H, W = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
filter_1 = filter if filter is not None else C
y = tf_helper.add_conv2d(x, filter_1, h_kernel=3, w_kernel=3, name=scope, h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv_input : ", y.shape)
if layer_list is not None:
layer_list.append(y)
return y
def feature_out(x, classes_num, format="NHWC", name="feature_out", batch_normalization=True,
layer_list=None):
"""
:param x:
:param classes_num:
:param format:
:param name:
:param batch_normalization:
:param layer_list:
:return:
"""
with tf.name_scope(name) as scope:
x = tf.convert_to_tensor(x)
input_shape = x.get_shape()
if format == "NHWC":
N, H, W, C = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
elif format == "NCHW":
N, C, H, W = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
filter_1 = (classes_num + 5) * 3
feature = tf_helper.add_conv2d(x, filter_1, h_kernel=1, w_kernel=1, name=scope, h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="linear", leaky_relu_alpha=0.1, padding="SAME")
print("conv_feature : ", feature.shape)
if layer_list is not None:
layer_list.append(feature)
return feature
def residual_block(x, filter=None, format="NHWC", name="residual_block", batch_normalization=True,
layer_list=None):
"""
:param x: input tensor
:param filter: first conv2d layer filter size. if None, it will be a half of the input tensor channel size.
:param format: "NHWC" for channel last and "NCHW" for channel first. default is 'NHWC'
:param name:
:param batch_normalization:
:param layer_list:
:return:
"""
with tf.name_scope(name) as scope:
x = tf.convert_to_tensor(x)
shortcut = x
input_shape = x.get_shape()
N, H, W, C = (0, 0, 0, 0)
if format == "NHWC":
N, H, W, C = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
elif format == "NCHW":
N, C, H, W = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
filter_1 = filter if filter is not None else int(C / 2)
filter_2 = C
block_conv_1 = tf_helper.add_conv2d(x, filter_1, h_kernel=1, w_kernel=1, name="layer_1", h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv_residual : ", block_conv_1.shape)
block_conv_2 = tf_helper.add_conv2d(block_conv_1, filter_2, h_kernel=3, w_kernel=3, name="layer_2",
h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv_residual : ", block_conv_2.shape)
y = tf_helper.add_shortcut(block_conv_2, shortcut, name=scope)
print("shortcut : ", y.shape)
if layer_list is not None:
layer_list.append(block_conv_1)
layer_list.append(block_conv_2)
return y
def __init__(self,
input_size=784,
mid_units=100,
output_size=10,
lr=1e-4,
keep_prob=0.5,
output_op_name="output"):
super(CONV, self).__init__()
self.input_size = input_size
self.mid_units = mid_units
self.output_size = output_size
self.lr = lr
self.output_node_name = output_op_name
mlp_graph = tf.Graph()
with mlp_graph.as_default():
x = tf.placeholder(tf.float32, [None, self.input_size],
name="input")
y_t = tf.placeholder(tf.float32, [None, self.output_size],
name="valid")
x_image = tf.reshape(x, [-1, 28, 28, 1], name="reshape")
conv1 = tf_ext.add_conv2d(x_image,
output_size=32,
h_kernel=5,
w_kernel=5,
activation="relu",
name="conv1")
pool1 = tf_ext.add_pool(conv1, name="pool1")
conv2 = tf_ext.add_conv2d(pool1,
output_size=64,
h_kernel=5,
w_kernel=5,
activation="relu",
name="conv2")
pool2 = tf_ext.add_pool(conv2, name="pool2")
flat = tf_ext.add_flatten(pool2, name="flatten")
fc1 = tf_ext.add_fc(flat,
output_size=1024,
activation="relu",
name="fc1")
drop = tf_ext.add_dropout(fc1,
keep_prob=keep_prob,
name="dropout1")
fc2 = tf_ext.add_fc(drop,
self.output_size,
activation="relu",
name="fc2")
y = tf.nn.softmax(fc2, name=self.output_node_name)
with tf.name_scope("train"):
cross_entropy = -tf.reduce_sum(y_t * tf.log(y))
train_op = tf.train.AdamOptimizer(
learning_rate=self.lr).minimize(cross_entropy)
with tf.name_scope("predict"):
correct_prediction = tf.equal(tf.argmax(y, 1),
tf.argmax(y_t, 1))
predict_op = tf.reduce_mean(
tf.cast(correct_prediction, tf.float32))
# version V2 ファイル新規作成する際にエラーになります。
# max_to_keep 出力ckpt フィアル数
saver = tf.train.Saver(write_version=tf.train.SaverDef.V1,
max_to_keep=1)
pb_saver = tf.train
init = tf.global_variables_initializer()
self.graph = mlp_graph
self.train_op = train_op
self.predict_op = predict_op
self.loss_op = cross_entropy
self.x = x
self.y = y
self.y_t = y_t
self.init = init
self.saver = saver
self.pb_saver = pb_saver
def conv2d_5l_block(x, filter=None, format="NHWC", name="conv_5l", batch_normalization=True,
layer_list=None):
"""
:param x:
:param filter:
:param format:
:param name:
:param batch_normalization:
:param layer_list:
:return:
"""
with tf.name_scope(name) as scope:
x = tf.convert_to_tensor(x)
input_shape = x.get_shape()
N, H, W, C = (0, 0, 0, 0)
if format == "NHWC":
N, H, W, C = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
elif format == "NCHW":
N, C, H, W = input_shape[0].value, input_shape[1].value, input_shape[2].value, input_shape[3].value
filter_1 = filter if filter is not None else int(C / 2)
filter_2 = int(filter_1 * 2)
filter_3 = filter_1
filter_4 = int(filter_1 * 2)
filter_5 = filter_1
block_conv_1 = tf_helper.add_conv2d(x, filter_1, h_kernel=1, w_kernel=1, name="layer_1", h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv5l : ", block_conv_1.shape)
block_conv_2 = tf_helper.add_conv2d(block_conv_1, filter_2, h_kernel=3, w_kernel=3, name="layer_2",
h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv5l : ", block_conv_2.shape)
block_conv_3 = tf_helper.add_conv2d(block_conv_2, filter_3, h_kernel=1, w_kernel=1, name="layer_3",
h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv5l : ", block_conv_3.shape)
block_conv_4 = tf_helper.add_conv2d(block_conv_3, filter_4, h_kernel=3, w_kernel=3, name="layer_4",
h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv5l : ", block_conv_4.shape)
block_conv_5 = tf_helper.add_conv2d(block_conv_4, filter_5, h_kernel=1, w_kernel=1, name="layer_5",
h_stride=1,
w_stride=1, format=format, batch_normalization=batch_normalization,
activation="leaky_relu", leaky_relu_alpha=0.1, padding="SAME")
print("conv5l : ", block_conv_5.shape)
y = block_conv_5
if layer_list is not None:
layer_list.append(block_conv_1)
layer_list.append(block_conv_2)
layer_list.append(block_conv_3)
layer_list.append(block_conv_4)
layer_list.append(block_conv_5)
return y
