why validation accuracy is zero?

[colab-user]

In my dataset number of train images with class "0" is 3828 and number of train images with class "1" is 3740, and number of validation photos is 379. Using model is:

def baseline_model():

input_1 = Input(shape=(224, 224, 3))
input_2 = Input(shape=(224, 224, 3))

base_model = VGGFace(model='resnet50', include_top=False) 

for x in base_model.layers[:-3]:
    x.trainable = True

x1 = base_model(input_1)
x2 = base_model(input_2)

x1 = Concatenate(axis=-1)([GlobalMaxPool2D()(x1), GlobalAvgPool2D()(x1)])
x2 = Concatenate(axis=-1)([GlobalMaxPool2D()(x2), GlobalAvgPool2D()(x2)])

x3 = Subtract()([x1, x2])
x3 = Multiply()([x3, x3])

x1_ = Multiply()([x1, x1])
x2_ = Multiply()([x2, x2])
x4 = Subtract()([x1_, x2_])
x = Concatenate(axis=-1)([x4, x3])

x = Dense(100, activation="relu")(x)
x = Dropout(0.01)(x)
out = Dense(1, activation="sigmoid")(x)#softmax

model = Model([input_1, input_2], out)

model.compile(loss="binary_crossentropy", optimizer=Adam(0.00001) , metrics=['accuracy']) # metrics=[f1_m, precision_m, recall_m]

model.summary()

return model

The result is:
loss: 3.0981 - accuracy: 0.9739 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00
Why val_loss and val_accuracy stuck at zero?

In Data_generator function I convert each batch of images to numpy array :
x_batch = np.array(x_batch)
x_batch1 = np.array(x_batch1)
y_batch = np.array(y[idd])
yield [x_batch,x_batch1], y_batch

How can I solve this problem?