Using TensorFlow backend.
C:\Users\lenovo-w\PycharmProjects\deepphos\methods\phosnet1.py:122: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(32, 1, activation="relu", padding="same", kernel_initializer="RandomUniform", kernel_regularizer=<keras.reg..., use_bias=False)`
W_regularizer=l2(weight_decay))(main_input)
2020-09-21 19:09:41.384350: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2020-09-21 19:09:41.405531: I tensorflow/core/common_runtime/process_util.cc:147] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
C:\Users\lenovo-w\PycharmProjects\deepphos\methods\phosnet1.py:30: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(32, 13, activation="relu", padding="same", kernel_initializer="RandomUniform", kernel_regularizer=<keras.reg..., use_bias=False)`
W_regularizer=l2(weight_decay))(x)
Traceback (most recent call last):
File "C:/Users/lenovo-w/PycharmProjects/deepphos/predict.py", line 82, in <module>
hierarchy='group', kinase='AGC')
File "C:/Users/lenovo-w/PycharmProjects/deepphos/predict.py", line 56, in predict_for_deepphos
model = model_net(X_test1, X_test2, X_test3, y_test,nb_epoch = 0)
File "C:\Users\lenovo-w\PycharmProjects\deepphos\methods\model_n.py", line 58, in model_net
dense_number, dropout_rate, dropout_dense, weight_decay)
File "C:\Users\lenovo-w\PycharmProjects\deepphos\methods\phosnet1.py", line 136, in Phos
weight_decay=weight_decay)
File "C:\Users\lenovo-w\PycharmProjects\deepphos\methods\phosnet1.py", line 87, in denseblock
x = merge(list_feat, mode='concat', concat_axis=concat_axis)
TypeError: 'module' object is not callable
Process finished with exit code 1
报的错误
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D, AveragePooling1D
from keras.layers.pooling import GlobalAveragePooling2D,GlobalAveragePooling1D
from keras.layers import Input, merge, Flatten
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
import keras.backend as K
from keras.layers import Conv1D,Conv2D, MaxPooling2D
def conv_factory(x, init_form, nb_filter, filter_size_block, dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 3x3Conv2D, optional dropout
:param x: Input keras network
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: keras network with b_norm, relu and convolution2d added
:rtype: keras network
"""
#x = Activation('relu')(x)
x = Conv1D(nb_filter, filter_size_block,
init=init_form,
activation='relu',
border_mode='same',
bias=False,
W_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def transition(x, init_form, nb_filter, dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 1x1Conv2D, optional dropout and Maxpooling2D
:param x: keras model
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: model
:rtype: keras model, after applying batch_norm, relu-conv, dropout, maxpool
"""
#x = Activation('relu')(x)
x = Conv1D(nb_filter, 1,
init=init_form,
activation='relu',
border_mode='same',
bias=False,
W_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
#x = AveragePooling2D((2, 2),padding='same')(x)
x = AveragePooling1D(pool_size=2, padding='same')(x)
return x
def denseblock(x, init_form, nb_layers, nb_filter, growth_rate,filter_size_block,
dropout_rate=None, weight_decay=1E-4):
"""Build a denseblock where the output of each
conv_factory is fed to subsequent ones
:param x: keras model
:param nb_layers: int -- the number of layers of conv_
factory to append to the model.
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: keras model with nb_layers of conv_factory appended
:rtype: keras model
"""
list_feat = [x]
concat_axis = -1
for i in range(nb_layers):
x = conv_factory(x, init_form, growth_rate, filter_size_block, dropout_rate, weight_decay)
list_feat.append(x)
x = merge(list_feat, mode='concat', concat_axis=concat_axis)
nb_filter += growth_rate
return x
def Phos(nb_classes, nb_layers,img_dim1,img_dim2,img_dim3, init_form, nb_dense_block,
growth_rate,filter_size_block1,filter_size_block2,filter_size_block3,
nb_filter, filter_size_ori,
dense_number,dropout_rate,dropout_dense,weight_decay):
""" Build the DenseNet model
:param nb_classes: int -- number of classes
:param img_dim: tuple -- (channels, rows, columns)
:param depth: int -- how many layers
:param nb_dense_block: int -- number of dense blocks to add to end
:param growth_rate: int -- number of filters to add
:param nb_filter: int -- number of filters
:param dropout_rate: float -- dropout rate
:param weight_decay: float -- weight decay
:param nb_layers:int --numbers of layers in a dense block
:param filter_size_ori: int -- filter size of first conv1d
:param dropout_dense: float---drop out rate of dense
:returns: keras model with nb_layers of conv_factory appended
:rtype: keras model
"""
# first input of 33 seq #
main_input = Input(shape=img_dim1)
#model_input = Input(shape=img_dim)
# Initial convolution
x1 = Conv1D(nb_filter, filter_size_ori,
init = init_form,
activation='relu',
border_mode='same',
bias=False,
W_regularizer=l2(weight_decay))(main_input)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x1 = denseblock(x1, init_form, nb_layers, nb_filter, growth_rate,filter_size_block1,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
# add transition
x1 = transition(x1, init_form, nb_filter, dropout_rate=dropout_rate,
weight_decay=weight_decay)
# The last denseblock does not have a transition
x1 = denseblock(x1, init_form, nb_layers, nb_filter, growth_rate,filter_size_block1,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
x1 = Activation('relu',name='seq1')(x1)
# second input of 21 seq #
input2 = Input(shape=img_dim2)
x2 = Conv1D(nb_filter, filter_size_ori,
init=init_form,
activation='relu',
border_mode='same',
bias=False,
W_regularizer=l2(weight_decay))(input2)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x2 = denseblock(x2, init_form, nb_layers, nb_filter, growth_rate, filter_size_block2,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
# add transition
x2 = transition(x2, init_form, nb_filter, dropout_rate=dropout_rate,
weight_decay=weight_decay)
# The last denseblock does not have a transition
x2 = denseblock(x2, init_form, nb_layers, nb_filter, growth_rate, filter_size_block2,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
x2 = Activation('relu')(x2)
#third input seq of 15 #
input3 = Input(shape=img_dim3)
x3 = Conv1D(nb_filter, filter_size_ori,
init=init_form,
activation='relu',
border_mode='same',
bias=False,
W_regularizer=l2(weight_decay))(input3)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x3 = denseblock(x3, init_form, nb_layers, nb_filter, growth_rate, filter_size_block3,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
# add transition
x3 = transition(x3, init_form, nb_filter, dropout_rate=dropout_rate,
weight_decay=weight_decay)
# The last denseblock does not have a transition
x3 = denseblock(x3, init_form, nb_layers, nb_filter, growth_rate, filter_size_block3,
dropout_rate=dropout_rate,
weight_decay=weight_decay)
x3 = Activation('relu')(x3)
# contact 3 output features #
x = merge([x1, x2, x3], mode='concat', concat_axis=-2, name='contact_multi_seq')
#x = GlobalAveragePooling1D()(x)
x = Flatten()(x)
x = Dense(dense_number,
name ='Dense_1',
activation='relu',init = init_form,
W_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay))(x)
x = Dropout(dropout_dense)(x)
#softmax
x = Dense(nb_classes,
name = 'Dense_softmax',
activation='softmax',init = init_form,
W_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay))(x)
phos_model = Model(input=[main_input,input2,input3], output=[x], name="multi-DenseNet")
#feauture_dense = Model(input=[main_input, input2, input3], output=[x], name="multi-DenseNet")
return phos_modelkeras 2.3.1
第一个问题是可能出现了代码问题，第二个问题是，为什么一直显示'module' object is not callable，检查sys路径是没有任何问题的，而且对应文件是放在一个文件夹和包里的
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