title: Model Evaluate a fixed accuracy 0.5000
body: <p>I am training a CNN model using Keras on Google Colab for binary image classification, the problem is when I use the test dataset divided into 2 classes in the model evaluate the accuracy is fixed at 0.5000. When I use a test dataset without dividing the data into 2 classes this does not happen and I have an accuracy of 0.9167.</p>
  <p>My code:</p>
  <pre><code>
  modelo.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(551, 1117, 3)))
  modelo.add(MaxPooling2D((2, 2)))
  
  modelo.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  modelo.add(MaxPooling2D((2, 2)))
  
  modelo.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  modelo.add(MaxPooling2D((2, 2)))
  
  modelo.add(Flatten())
  modelo.add(Dense(128, activation='relu', kernel_initializer='he_uniform')) 
  modelo.add(Dense(1, activation='sigmoid'))
  
  modelo.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
  hist = modelo.fit(traning_generator, validation_data=validation_generator, epochs=10, callbacks=callbacks)
  </code></pre>
  <p>training result:</p>
  <pre><code>Epoch 1/10
  13/13 [==============================] - 33s 3s/step - loss: 136.5746 - accuracy: 0.5300 - val_loss: 23.4487 - val_accuracy: 0.4068
  Epoch 2/10
  13/13 [==============================] - 27s 2s/step - loss: 5.3578 - accuracy: 0.4675 - val_loss: 0.7363 - val_accuracy: 0.4068
  Epoch 3/10
  13/13 [==============================] - 28s 2s/step - loss: 0.6870 - accuracy: 0.5925 - val_loss: 0.7120 - val_accuracy: 0.5932
  Epoch 4/10
  13/13 [==============================] - 18s 1s/step - loss: 0.5529 - accuracy: 0.7225 - val_loss: 0.8240 - val_accuracy: 0.3898
  Epoch 5/10
  13/13 [==============================] - 18s 1s/step - loss: 0.4633 - accuracy: 0.7750 - val_loss: 1.1202 - val_accuracy: 0.4322
  Epoch 6/10
  13/13 [==============================] - 19s 1s/step - loss: 0.5213 - accuracy: 0.7675 - val_loss: 1.4779 - val_accuracy: 0.4407
  Epoch 7/10
  13/13 [==============================] - 17s 1s/step - loss: 0.1730 - accuracy: 0.9550 - val_loss: 1.8047 - val_accuracy: 0.4492
  Epoch 8/10
  13/13 [==============================] - 17s 1s/step - loss: 0.0887 - accuracy: 0.9925 - val_loss: 2.4989 - val_accuracy: 0.4831
  Epoch 9/10
  13/13 [==============================] - 17s 1s/step - loss: 0.0318 - accuracy: 1.0000 - val_loss: 3.7380 - val_accuracy: 0.4407
  Epoch 10/10
  13/13 [==============================] - 17s 1s/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 4.7144 - val_accuracy: 0.4492 
  </code></pre>
  <p>when testing dataset without class division I get:</p>
  <pre><code>test_loss, test_acc = modelo.evaluate(test_dataset)
  
  1/1 [==============================] - 1s 577ms/step - loss: 0.6590 - accuracy: 0.9167
  </code></pre>
  <p>and when testing the test dataset with 2 classes:</p>
  <pre><code>2/2 [==============================] - 3s 653ms/step - loss: 1.2334 - accuracy: 0.5000
  </code></pre>