问题
I have tried to write some example with keras,but some error happenError when checking target: expected dense_2 to have shape (2,) but got array with shape (1,)
I have tried to change the input_shape but it doesn't work
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
import numpy
print "hello"
input=[[1],[2],[3],[4],[5],[6],[7],[8]]
input=numpy.array(input, dtype="float")
# input=input.reshape(8,1)
output=[[1],[0],[1],[0],[1],[0],[1],[0]]
output=numpy.array(output, dtype="float")
(trainx,testx,trainy,testy)=train_test_split(input, output, test_size=0.25, random_state=42)
lb = LabelBinarizer()
trainy=lb.fit_transform(trainy)
testy=lb.transform(testy)
model=Sequential()
model.add(Dense(4,input_shape=(1,),activation="sigmoid"))
# model.add(Dense(4,activation="sigmoid"))
# print len(lb.classes_)
model.add(Dense(len(lb.classes_),activation="softmax",input_shape=(4,)))
INIT_LR = 0.01
EPOCHS = 20
print("[INFO] training network...")
opt = SGD(lr=INIT_LR)
model.compile(loss="categorical_crossentropy", optimizer=opt,metrics=["accuracy"])
H = model.fit(trainx, trainy, validation_data=(testx, testy),epochs=EPOCHS, batch_size=2)
回答1:
Since you have two classes, you can have a single neuron in the final Dense layer and use sigmoid activation. Or if you want to use softmax, you need to create a one hot encoding of y like this.
(trainx,testx,trainy,testy)=train_test_split(input, output, test_size=0.25, random_state=42)
trainy = keras.utils.to_categorical(trainy, 2)
testy = keras.utils.to_categorical(testy, 2)
回答2:
You should use "from tensorflow.python.keras.xx" instead of "from keras.xx". It prevents it from receiving the error like: "AttributeError: module 'tensorflow' has no attribute 'get_default_graph"
来源：https://stackoverflow.com/questions/55349650/keras-errorerror-when-checking-target-expected-dense-2-to-have-shape-2-but