Why Keras raises shape error in last dense layer? - tensorflow

Question

I built a simple NN to distinguish integers from decimals, my input data is 1 dimensional array,and the final output should be the probability of integer.
At first, I succeeded when last layer(name:output) had 1 unit. But it raised ValueError when I changed the last dense layer to two units,for I wanted to output both probabilities of number x as integer and decimal.
from tensorflow.python.keras.models import Sequential,load_model
from tensorflow.python.keras.utils import np_utils
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.layers import Activation
from tensorflow import keras
import numpy as np
import tensorflow as tf
from sklearn.utils import shuffle
def train():
t=[]
a=[]
for i in range (0,8000): #generate some training data
ran=np.random.randint(2)
if(ran==0):
y=np.random.uniform(-100,100)
t.append(y)
a.append(0)
else:
y=np.random.randint(1000)
t.append(y)
a.append(1)
t=np.asarray(t)
a=np.asarray(a)
pt=t.reshape(-1,1) #reshape for fit()
pa=a.reshape(-1,1)
pt,pa=shuffle(pt,pa)
model=Sequential()
dense=Dense(units=32,input_shape=(1,),activation='relu')
dense2=Dense(units=64,activation='relu')
output=Dense(units=2,activation='softmax') # HERE is the problem
model.add(dense)
model.add(dense2)
model.add(output)
model.summary()
model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
model.fit(pt,pa,validation_split=0.02,batch_size=10, epochs=50, verbose=2)
model.save('integer_predictor.h5')
train()
ValueError: Error when checking target: expected dense_2 to have shape (2,) but got array with shape (1,)

Answer 1

This should solve your problem
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
Since you have 2 outputs, you cant use binary cross_entropy since its a 2 class classification problem. Also, when your inputs are not one-hot encoded you will need sparse_categorical_crossentropy. If you have one hot features then categorical_crossentropy will work with outputs > 1.
Read this to get more insight into this.