由于我是新手，请帮我解决。几个月以来我一直在尝试。请忽略任何小错误并集中注意力。提前致谢。自从我得到这段代码以来，我一直在重塑，现在到达最底层，但仍然得到上面提到的这个错误作为标题。如果您需要任何 Hd5 文件，请告诉我您的电子邮件 ID。
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
from __future__ import print_function, division
import numpy as np
import h5py
import scipy.io
import random
import sys,os
import itertools
import numbers
from collections import Counter
from warnings import warn
from abc import ABCMeta, abstractmethod
from tensorflow import keras, reshape
np.random.seed(1337)
import keras
from keras.optimizers import RMSprop, SGD
from keras.models import Sequential, model_from_yaml
from keras.layers.core import Dense, Dropout, Activation, Flatten
import keras.layers.core as core
from tensorflow.keras.layers import Dense, Dropout, Embedding, LSTM, Input, multiply, Reshape
from keras.layers.convolutional import Convolution1D, MaxPooling1D
from keras.layers.wrappers import Bidirectional
from keras.constraints import maxnorm
from keras.layers.recurrent import LSTM, GRU
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.layers import Embedding
from sklearn.metrics import fbeta_score, roc_curve, auc, roc_auc_score, average_precision_score
import matplotlib.pyplot as plt
from keras.regularizers import l2, l1, l1_l2
from tensorflow.keras.models import Model
import tensorflow.keras.backend as K
from keras.engine.topology import Layer
from keras import activations, initializers, regularizers, constraints
from keras.layers import Input
from keras.layers import ActivityRegularization
tf.compat.v1.disable_v2_behavior()
class Attention(tf.keras.layers.Layer):
def __init__(self,hidden ,init='glorot_uniform',activation='linear',W_regularizer=None,b_regularizer=None,W_constraint=None,**kwargs):
self.init = initializers.get(init)
self.activation = activations.get(activation)
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.hidden=hidden
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):
input_dim = input_shape[-1]
self.input_length = input_shape[1]
self.W0 = self.add_weight(name ='{}_W1'.format(self.name), shape = (input_dim, self.hidden), initializer = 'glorot_uniform', trainable=True) # Keras 2 API
self.W = self.add_weight( name ='{}_W'.format(self.name), shape = (self.hidden, 1), initializer = 'glorot_uniform', trainable=True)
self.b0 = K.zeros((self.hidden,), name='{}_b0'.format(self.name))
self.b = K.zeros((1,), name='{}_b'.format(self.name))
# self.trainable_weights = [self.W0,self.W,self.b,self.b0]
self.regularizers =[]
if self.W_regularizer:
self.W_regularizer.set_param(self.W)
self.regularizers.append(self.W_regularizer)
if self.b_regularizer:
self.b_regularizer.set_param(self.b)
self.regularizers.append(self.b_regularizer)
self.constraints = {}
if self.W_constraint:
self.constraints[self.W0] = self.W_constraint
self.constraints[self.W] = self.W_constraint
super(Attention, self).build(input_shape)
def call(self,x,mask=None):
attmap = self.activation(K.dot(x, self.W0)+self.b0)
print("self.b.shape=",self.b.shape)
attmap = K.dot(attmap, self.W) + self.b
print("attmap.shape=",attmap.shape)
#till now it was for attention fully connected network/dot product
attmap = K.reshape(attmap, (-1, self.input_length))
attmap = K.softmax(attmap)
print("attmap.shape1=",attmap.shape)
print("x.shape1=",x.shape)
dense_representation = K.batch_dot(attmap, x, axes=(1, 1))
print("dense_representation.shape=",dense_representation.shape)
out = K.concatenate([dense_representation, attmap],axis=1)
print("out.shape=",out.shape)
return out
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[-1] + input_shape[1])
def get_config(self):
config = {'init': 'glorot_uniform',
'activation': self.activation.__name__,
'W_constraint': self.W_constraint.get_config() if self.W_constraint else None,
'W_regularizer': self.W_regularizer.get_config() if self.W_regularizer else None,
'b_regularizer': self.b_regularizer.get_config() if self.b_regularizer else None,
'hidden': self.hidden if self.hidden else None}
base_config = super(Attention, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class attention_flatten(tf.keras.layers.Layer):
def __init__(self, keep_dim, **kwargs):
self.keep_dim = keep_dim
super(attention_flatten, self).__init__(**kwargs)
def build(self, input_shape):
pass
def compute_output_shape(self, input_shape):
if not all(input_shape[1:]):
raise Exception('The shape of the input to "Flatten" '
'is not fully defined '
'(got ' + str(input_shape[1:]) + '. '
'Make sure to pass a complete "input_shape" '
'or "batch_input_shape" argument to the first '
'layer in your model.')
return (input_shape[0], self.keep_dim)
def call(self, x, mask=None):
x=x[1:,:self.keep_dim]
return (x)
def set_up_model_up():
print('building model')
seq_input_shape = (2000,4,)
nb_filter = 64
filter_length = 6
input_shape = (2000,4,)
attentionhidden = 256
seq_input = Input(shape = seq_input_shape, name = 'seq_input')
convul1 = tf.keras.layers.Convolution1D(filters = nb_filter,
kernel_size = filter_length,
padding = 'valid',
activation = 'relu',
kernel_constraint = maxnorm(3),
)
# modil = Sequential()
pool_ma1 = keras.layers.MaxPooling1D(pool_size = 3)
dropout1 = Dropout(0.5977908689086315)
dropout2 = Dropout(0.30131233477637737)
decoder = Attention(hidden = attentionhidden, activation = 'linear')
dense1 = tf.keras.layers.Dense(1)
dense2 = tf.keras.layers.Dense(256)
output_1 = pool_ma1(convul1(seq_input))
output_2 = (output_1)
output_6=tf.reshape(output_2, [1,1995,64])
print("output_2's'shap[2]e=",output_2.shape[2])
print("output_2's'shape=",output_2.shape)
att_decoder = decoder(output_6)
# tf.slice(output_6, begin, size, name=None)
output_3 = attention_flatten(3)(att_decoder)
output_2=tf.reshape(output_2, [1,3, 42560])
output_4 = dense1(((output_2)))
output_4=tf.keras.layers.Flatten()(output_4)
print("output_3p.shape=",output_3.shape)
print("output_4p.shape=",output_4.shape)
all_outp = K.concatenate([output_3, output_4],axis=0)
print(all_outp)
print("all_outp.shape",all_outp.shape)
output_5 = dense2(all_outp)
tf.keras.layers.Add()
output_f = tf.keras.layers.Activation('sigmoid')(output_5)
output_c=tf.keras.layers.Add()(output_f)
modil=tf.keras.models.Model(inputs = seq_input, outputs = output_f)
modil.build(input_shape=input_shape)
modil.compile(loss = 'binary_crossentropy', optimizer = 'nadam', metrics = ['accuracy'])
print (modil.summary())
print("len(modil number of layers)",len(modil.layers))
return modil
def test(n_estimators = 16):
model = set_up_model_up()
model.save_weights('Sequential_model_weights.h5')
model.load_weights('Sequential_model_weights.h5',by_name=True)
X_test = np.load('/content/drive/MyDrive/X_test.npy', mmap_mode='r')
y_test = np.load('/content/drive/MyDrive/y_test.npy', mmap_mode='r')
ensemble = np.zeros(len(X_test))
for i in range(n_estimators):
print ('testing', i, 'model')
print ('model shape is', model.summary)
model.load_weights('/content/drive/MyDrive/model/bestmodel_split_chr_GD_'+ str(i) + '.hdf5')
print ('model shape after loading is', model.summary)
print ('Predicting...')
print ('testing',X_test.shape)
print (len(model.layers))
# y_score = model.predict(np.expand_dims(np.array(X_test, dtype=np.float32), 0), verbose = 1, batch_size = 256)
formatmul= np.empty((3,2000,4), dtype=object)
for x in range(0, 2):
for y in range(0, 1999):
for z in range(0, 3):
formatmul[x][y][z]=X_test[x][y][z]
# y_score = model.predict(X_test).reshape(665,-1), verbose = 1, batch_size = 256)
print("model.output_shape",model.output_shape)
print("model.input_shape",model.input_shape)
# y_score = model.predict(formatmul, batch_size=42560)
y_score = model.predict(np.array(formatmul, dtype=np.float32), batch_size =64)
y_pred = []
for item in y_score:
y_pred.append(item[0])
y_pred = np.array(y_pred)
ensemble += y_pred
ensemble /= n_estimators
np.save('/content/drive/MyDrive/test_result/y_test', y_test)
np.save('/content/drive/MyDrive/test_result/y_pred', ensemble)
auroc = roc_auc_score(y_test, ensemble)
aupr = average_precision_score(y_test, ensemble)
print ('auroc', auroc)
print ('aupr' , aupr)
test(n_estimators = 16)
堆栈跟踪：
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
building model
output_2's'shap[2]e= 64
output_2's'shape= (?, 665, 64)
self.b.shape= (1,)
attmap.shape= (1, 1995, 1)
attmap.shape1= (1, 1995)
x.shape1= (1, 1995, 64)
dense_representation.shape= (1, 64)
out.shape= (1, 2059)
output_3p.shape= (0, 3)
output_4p.shape= (1, 3)
Tensor("concat:0", shape=(1, 3), dtype=float32)
all_outp.shape (1, 3)
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
conv1d (Conv1D) (None, 1995, 64) 1600 seq_input[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape (TensorFlow [(1, 1995, 64)] 0 max_pooling1d[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_1 (TensorFl [(1, 3, 42560)] 0 max_pooling1d[0][0]
__________________________________________________________________________________________________
attention (Attention) (1, 2059) 16897 tf_op_layer_Reshape[0][0]
__________________________________________________________________________________________________
dense (Dense) (1, 3, 1) 42561 tf_op_layer_Reshape_1[0][0]
__________________________________________________________________________________________________
attention_flatten (attention_fl (0, 3) 0 attention[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (1, 3) 0 dense[0][0]
__________________________________________________________________________________________________
tf_op_layer_concat (TensorFlowO [(1, 3)] 0 attention_flatten[0][0]
flatten[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (1, 256) 1024 tf_op_layer_concat[0][0]
__________________________________________________________________________________________________
activation (Activation) (1, 256) 0 dense_1[0][0]
==================================================================================================
Total params: 62,082
Trainable params: 62,082
Non-trainable params: 0
__________________________________________________________________________________________________
None
len(modil number of layers) 10
testing 0 model
model shape is <bound method Model.summary of <tensorflow.python.keras.engine.functional.Functional object at 0x7f5faf959350>>
model shape after loading is <bound method Model.summary of <tensorflow.python.keras.engine.functional.Functional object at 0x7f5faf959350>>
Predicting...
testing (172832, 2000, 4)
10
model.output_shape (1, 256)
model.input_shape (None, 2000, 4)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2426: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
warnings.warn('`Model.state_updates` will be removed in a future version. '
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-1-b9a05eeb9fa1> in <module>()
240 # if __name__ == '__main__':
241 # set_up_model_up()
--> 242 test(n_estimators = 16)
5 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1480 ret = tf_session.TF_SessionRunCallable(self._session._session,
1481 self._handle, args,
-> 1482 run_metadata_ptr)
1483 if run_metadata:
1484 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)