Custom CNN

def HappyModel(input_shape):
  # Define the input placeholder as a tensor with shape input_shape.
  X_input = Input(input_shape, name='input')

  # Zero-Padding: pads the border of X_input with zeroes
  X = ZeroPadding2D((3, 3), name='padding')(X_input)

  # CONV -> BN -> RELU Block applied to X
  X = Conv2D(32, (7, 7), strides=(1, 1), name='conv1')(X)
  X = BatchNormalization(axis=3, name='bn1')(X)
  X = Activation('relu', name='relu1')(X)

  # MAXPOOL
  X = MaxPooling2D((2, 2), name='max_pool1')(X)

  # FLATTEN X (means convert it to a vector) + FULLYCONNECTED
  X = Flatten(name='flatten1')(X)
  X = Dense(1, activation='sigmoid', name='fc1-output')(X)

  # Create model. This creates our Keras model instance, you’ll use this instance
  # to train/test the model.
  model = Model(inputs=X_input, outputs=X, name='HappyModel')
  return model

happyModel = HappyModel(X_train.shape[1:])
happyModel.summary()