ValueError：“顺序”层的输入 0 与该层不兼容：预期形状=（无，256、256、3），找到的形状=（无，33）

Question

I started running dukkakc's code about DeepSuccinySite and i have a problem. The result show: ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 256, 256, 3), found shape=(None, 33). I've been searching for the error for a long time but to no avail. Can someone help me?

from tensorflow import keras
import numpy as np
from Bio import SeqIO
from numpy import array
from imblearn.under_sampling import RandomUnderSampler
from sklearn.utils import shuffle
from sklearn.metrics import roc_curve, auc, classification_report
from keras.models import load_model

r_test_x = []
r_test_y = []
posit_1 = 1;
negat_0 = 0;
win_size = 33 # actual window size
win_size_kernel = int(win_size/2 + 1)

# define universe of possible input values
alphabet = 'ARNDCQEGHILKMFPSTWYV-'
# define a mapping of chars to integers
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))

#-------------------------TEST DATASET----------------------------------------
#for positive sequence
def innertest1():
#Input
data = seq_record.seq
#rint(data) 
# integer encode input data
for char in data:
    if char not in alphabet:
        return
integer_encoded = [char_to_int[char] for char in data]
r_test_x.append(integer_encoded)
r_test_y.append(posit_1)
for seq_record in SeqIO.parse("test_positive_sites.fasta", "fasta"):
innertest1()
#for negative sequence
def innertest2():
#Input
data = seq_record.seq
#print(data) 
# integer encode input data
for char in data:
    if char not in alphabet:
        return
integer_encoded = [char_to_int[char] for char in data]
r_test_x.append(integer_encoded)
r_test_y.append(negat_0)
for seq_record in SeqIO.parse("test_negative_sites.fasta", "fasta"):
innertest2()
# Changing to array (matrix)    
r_test_x = array(r_test_x)
r_test_y = array(r_test_y)

# Balancing test dataset
# Testing Data Balancing by undersampling####################################
rus = RandomUnderSampler(random_state=7)
x_res3, y_res3 = rus.fit_resample(r_test_x, r_test_y)
#Shuffling
r_test_x, r_test_y = shuffle(x_res3, y_res3, random_state=7)
r_test_x = np.array(r_test_x)
r_test_y = np.array(r_test_y)
############################################################################

##LOAD MODEL####
model = load_model('model.h5')
#print("This is final ",model.layers[0].get_weights()[0][16])
r_test_y_2 = keras.utils.to_categorical(r_test_y, 2)
score = model.evaluate(r_test_x, r_test_y_2, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import confusion_matrix
Y_pred = model.predict(r_test_x)
Y_pred = (Y_pred > 0.5)
y_pred1 = [np.argmax(y, axis=None, out=None) for y in Y_pred]
y_pred1 = np.array(y_pred1)

print("Matthews Correlation : ",matthews_corrcoef(r_test_y, y_pred1))
print("Confusion Matrix : \n",confusion_matrix(r_test_y, y_pred1))
# ROC

fpr, tpr, _ = roc_curve(r_test_y, y_pred1)
roc_auc = auc(fpr, tpr)
print("AUC : ", roc_auc)
print(classification_report(r_test_y, y_pred1))

print(model.summary())

Answer 1

Your input has to match the expected format of the input layer of your model. You can use reshape to get the right format, expected the dimension match, otherwise you'll need to pad the missing dimensions.

import numpy as np

A = np.random.rand(16,1,1)
A.shape
  (16,1,1)

B = A.reshape([4,4,1,1]
B.shape
  (4,4,1,1)