def inference(x,sequence_len,training,full_sequence_len,configure, apply_ratio = False):
"""Infer a logits of the input signal batch.
Args:
x: Tensor of shape [batch_size, max_time,channel], a batch of the input signal with a maximum length `max_time`.
sequence_len: Tensor of shape [batch_size], given the real lenghs of the segments.
training: Placeholder of Boolean, Ture if the inference is during training.
full_sequence_len: Scalar float, the maximum length of the sample in the batch.
configure:Model configuration.
apply_ratio: If apply the ration to the sequence_len or not.
Returns:
logits: Tensor of shape [batch_size, max_time, class_num]
ratio: Scalar float, the scale factor between the output logits and the input maximum length.
"""
cnn_feature = getcnnfeature(x,training = training,cnn_config = configure['cnn']['model'])
feashape = cnn_feature.get_shape().as_list()
ratio = full_sequence_len/feashape[1]
if apply_ratio:
sequence_len = tf.cast(tf.ceil(tf.cast(sequence_len,tf.float32)/ratio),tf.int32)
if configure['rnn']['layer_num'] == 0:
logits = getcnnlogit(cnn_feature)
else:
logits = rnn_layers(cnn_feature,
sequence_len,
training,
layer_num = 3,
hidden_num = 100,
cell="SRU")
#logits = cudnn_rnn(cnn_feature,rnn_layer_num)
return logits,ratio
def inference(x,seq_length,training):
cnn_feature = getcnnfeature(x,training = training)
feashape = cnn_feature.get_shape().as_list()
ratio = FLAGS.sequence_len/feashape[1]
# logits = rnn_layers(cnn_feature,seq_length/ratio,training,class_n = 4**FLAGS.k_mer+1 )
# logits = rnn_layers_one_direction(cnn_feature,seq_length/ratio,training,class_n = 4**FLAGS.k_mer+1 )
logits = getcnnlogit(cnn_feature)
return logits,ratio
def inference(x, seq_len, training):
"""Infer a logits of the input signal batch.
Args:
x: Tensor of shape [batch_size, max_time], a batch of the input signal with a maximum length `max_time`.
seq_len: Scalar float, the maximum length of the sample in the batch.
training: Placeholder of Boolean, Ture if the inference is during training.
Returns:
logits: Tensor of shape [batch_size, max_time, class_num]
ratio: Scalar float, the scale factor between the output logits and the input maximum length.
"""
cnn_feature = getcnnfeature(x, training=training)
feashape = cnn_feature.get_shape().as_list()
ratio = seq_len / feashape[1]
logits = getcnnlogit(cnn_feature)
return logits, ratio
def inference(x,sequence_len,training):
cnn_feature = getcnnfeature(x,training = training)
feashape = cnn_feature.get_shape().as_list()
ratio = sequence_len/feashape[1]
logits = getcnnlogit(cnn_feature)
return logits,ratio
def inference(x):
logits = getcnnlogit(x, outnum=5)
return logits
data[i, :, :, :] = im[:, :, :] / 255.0
cc = (int(l) - 1) % ab_size
label[i, cc] = 1
return data, label
if __name__ == '__main__':
ab_size = 3817
bs = 32
imsize = [32, 32, 3]
im = tf.placeholder(tf.float32, shape=[
bs,
] + imsize)
label = tf.placeholder(tf.float32, shape=[bs, ab_size])
logit = getcnnlogit(im, ab_size)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=label, logits=logit))
correct_prediction = tf.equal(tf.argmax(logit, 1), tf.argmax(label, 1))
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
opt = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
trainlst = []
for line in open("datalist/train.lst"):
trainlst.append(line)
testlst = []
for line in open("datalist/test.lst"):
testlst.append(line)
saver = tf.train.Saver()
