title: why my Tensorflow CNN's accuracy is zero while loss is not?
body: <p>i am trying to make twin CNN,
  my database has two inputs that finally merge together and a single neuron as output of IC50.</p>
  
  <p>when i'm trying to do so, i get 0 accuracy while the loss is ok.
  am i using the wrong loss function? it is currently <code>mean_squared_error</code></p>
  
  <p>OS: Windows10
  tensorflow version:2.3.0</p>
  
  <p>my code"</p>
  
  <pre><code>encoded_drugs=np.load('encoded_drugs.npy')
  encoded_cells=np.load('encoded_cells.npy')
  encoded_ICs=np.load('encoded_ICs.npy')
  encoded_drugs_train, encoded_drugs_test,encoded_cells_train, encoded_cells_test, encoded_ICs_train, encoded_ICs_test = train_test_split(encoded_drugs,encoded_cells, encoded_ICs, test_size=0.2)
  
  
  input1=keras.layers.Input(shape=(139,32,))
  x1=keras.layers.Flatten(input_shape=(139,32,))(input1)
  x2=keras.layers.Dense(64,activation='relu')(x1)
  x3=keras.layers.Dense(64,activation='relu')(x2)
  
  input2=keras.layers.Input(shape=(735,2,))
  y1=keras.layers.Flatten(input_shape=(735,2,))(input2)
  y2=keras.layers.Dense(128,activation='relu')(y1)
  y3=keras.layers.Dense(64,activation='relu')(y2)
  
  merged=keras.layers.concatenate([x3,y3],axis=-1)
  
  z=keras.layers.Dense(64,activation='relu')(merged)
  out=keras.layers.Dense(1,activation='sigmoid')(z)
  
  model=keras.models.Model(inputs=[input1,input2], outputs=out)
  
  model.compile(optimizer='sgd',loss='mean_squared_error',metrics=['accuracy'])
  
  model.fit([encoded_drugs_train,encoded_cells_train],encoded_ICs_train,validation_split = 0.2,epochs=2)
  
  test_loss, test_accuracy= model.evaluate([encoded_drugs_test,encoded_cells_test],encoded_ICs_test)
  
  print('Accuracy=', test_accuracy)
  </code></pre>
  
  <p>my output:</p>
  
  <pre><code>2020-02-18 11:06:00.759824: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  AVX AVX2
  To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
  2020-02-18 11:06:00.774869: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.
  Train on 75793 samples, validate on 18949 samples
  Epoch 1/2
  75793/75793 [==============================] - 17s 229us/sample - loss: 10.3671 - accuracy: 0.0000e+00 - val_loss: 10.4082 - val_accuracy: 0.0000e+00
  Epoch 2/2
  75793/75793 [==============================] - 11s 146us/sample - loss: 10.2673 - accuracy: 0.0000e+00 - val_loss: 10.3852 - val_accuracy: 0.0000e+00
  
  
  3s 125us/sample - loss: 8.3239 - accuracy: 0.0000e+00
  Accuracy= 0.0
  </code></pre>