Question

错误：

  File "merge.py", line 48, in <module>
    model.fit(X_train, y_train, batch_size=128, nb_epoch=1, validation_split=0.2)
  File "/home/ngxin/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 1117, in fit
    batch_size=batch_size)
  File "/home/ngxin/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 1034, in _standardize_user_data
    exception_prefix='model target')
  File "/home/ngxin/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 112, in standardize_input_data
    str(array.shape))
ValueError: Error when checking model target: expected dense_1 to have 4 dimensions, but got array with shape (31000, 1)

谢谢

Answer 1

请看一下代码，开头可以保存的模型识别率是好的，但连接模型的第二阶段（连接相同的两个模型）非常糟糕，我认为应该是我的保存模型和连接问题的过程，但控制台中没有错误，谢谢！

(X_train, y_train), (X_test, y_test) = faces_load_data()#the data is an numpy array
model = Sequential()the procedure is not being given
model.add(Convolution2D(32, 3, 3,
                   border_mode='valid',
                   input_shape=(48, 48,1)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3,3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(7))
model.add(Activation('softmax'))

gen = ImageDataGenerator()
gen.fit(X_train)
train_generator =gen.flow(X_train,Y_train,batch_size=32,shuffle=False)
test_generator = gen.flow(X_test, Y_test, batch_size=32,shuffle=False)
train = model.predict_generator(train_generator, 31000)
test= model.predict_generator(test_generator, 3589)
with h5py.File("model_common.h5") as h:
    h.create_dataset("train", data=train)
    h.create_dataset("test", data=test)
    h.create_dataset("label", data=y_train)#sava mode ,there should hava a problem

for filename in ["model_common.h5","model_common.h5"]:
   with h5py.File(filename, 'r') as h:
        X_train.append(np.array(h['train'])) 
        X_test.append(np.array(h['test']))
        y_train = np.array(h['label'])
X_train = np.concatenate(X_train, axis=1)
X_test = np.concatenate(X_test, axis=1)
X_train, y_train = shuffle(X_train, y_train)
input_tensor = Input(X_train.shape[1:])
x = input_tensor
x = Dropout(0.5)(x)
x = Dense(1, activation='relu')(x)
model = Model(input_tensor, x)
model.fit(X_train,y_train, batch_size=32, nb_epoch=1,     validation_split=0.2)

连接h5fy重量有错误

1 个答案: