如何将自定义数据生成器输入到 model.fit 中，它会生成 X，y 和一个额外的数组，到 tensorflow.keras Z20F4ZF011622Z Z20F4ZF35E630DAF39466

Question

I am using CNN for classification problem. I have 3D images (CT scans) of patients and I am trying to predict the binary outcome on the basis of these images. I also have a clinical data and want to include that into the CNN model. I have a custom * Datagenerator (via keras.utils.Sequence) and it generates X, y, and also array of clinical data.

X,y will be used through out the model and would like to add clinical data in my second last dense layer (a layer prior to output layer)

Code for my Data generator

class DataGenerator(Sequence):
    'Generates data for Keras'

    def __init__(self, list_IDs, labels, clinfeat,batch_size=32, dim=(64, 64, 64), n_channels=1,
                 n_classes=1, shuffle=True, isTestData=False, images_per_id=1,isClinicalData=False,
                 base_train_image_path='finaldata/AllNumpyImages/',
                 base_test_images_path='testdata/'):
        'Initialization'
        self.dim = dim
        self.batch_size = batch_size
        self.labels = labels
        self.clinfeat = clinfeat
        self.list_repeated_ids = self.__get_repeated_list_ids(list_IDs, images_per_id)
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.shuffle = shuffle
        self.isTestData = isTestData
        self.isClinicalData = isClinicalData
        self.on_epoch_end()
        self.base_train_images_path = base_train_image_path
        self.base_test_images_path = base_test_images_path

    def __len__(self):
        'Denotes the number of batches per epoch'
        return len(self.list_repeated_ids) // self.batch_size

    def __getitem__(self, index):
        'Generate one batch of data'
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]

        # Find list of IDs
        list_ids_one_batch = [self.list_repeated_ids[k] for k in indexes]

        # Generate data
        if self.isClinicalData:
            X, y, clinical = self.__data_generation(list_ids_one_batch)
            return X, y, clinical
        else:
            X, y = self.__data_generation(list_ids_one_batch)
            return X, y


    def on_epoch_end(self):
        'Updates indexes after each epoch'
        self.indexes = np.arange(len(self.list_repeated_ids))
        if self.shuffle:
            np.random.shuffle(self.indexes)

    def __data_generation(self, list_ids_one_batch):
        'Generates data containing batch_size samples'  # X : (n_samples, *dim, n_channels)
        # Initialization
        X = np.empty((self.batch_size, *self.dim, self.n_channels))
        y = np.empty(self.batch_size, dtype=int)
        clinical = np.empty(shape=(self.batch_size,19), dtype=float)

        # Generate data
        for i, ID in enumerate(list_ids_one_batch):
            # Store sample
            if self.isTestData:
                X[i,] = np.load(os.path.join(self.base_test_images_path , ID)).reshape(64, 64, 64, 1)
            else:
                # generates random augmented image for each
                tmp_img = np.load(os.path.join(self.base_train_images_path, ID))
                aug_img = image_gen.random_transform(tmp_img)
                X[i,] = aug_img.reshape(64, 64, 64, 1)
            # Store class
            y[i] = self.labels[ID]
            if self.isClinicalData:
                clinical[i] = self.clinfeat[ID].values
                
        if self.isClinicalData:
            return X, y, clinical
        else:
            return X, y

    def __get_repeated_list_ids(self, list_ids, images_per_id):
        'Returns a new list of IDs where each ID is repeated @images_per_id times'
        list_repeated_images_ids = []
        for id in list_ids:
            list_repeated_images_ids.extend([id] * images_per_id)
        return  list_repeated_images_ids

This is my Model. I am using tensorboard for logging my metrics and hyperparameters

def create_model(hparams):
    model = Sequential()
        
    model.add(Conv3D(filters=64,kernel_size=(5,5,5),strides=(1,1,1),padding='valid',activation='relu',
                     kernel_regularizer=tf.keras.regularizers.l2(0.000001),input_shape = image_shape))
    #model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='same'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
    
    model.add(Conv3D(filters=128,kernel_size=(3,3,3),strides=(1,1,1),padding='valid',activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='valid'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
        
    model.add(Conv3D(filters=256,kernel_size=(3,3,3),strides=(1,1,1),padding='valid',activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    #model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='same'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
        
    model.add(Conv3D(filters=512,kernel_size=(3,3,3),strides=(1,1,1),padding='valid',activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='valid'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
        
    model.add(Flatten())
    
    model.add(Dense(hparams[HP_NUM_UNITS],activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
    DL = hparams[HP_NUM_DLAYER]
    DU = hparams[HP_NUM_UNITS]
    if DL == 2 and DU == 512:
        model.add(Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
    elif DL == 3 and DU == 512:
        model.add(Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
            
        model.add(Dense(128,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
            
    elif DL == 2 and DU == 1024:
        model.add(Dense(512,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
    else:
        model.add(Dense(512,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
        
        model.add(Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
        
            
    model.add(Dense(1,activation='sigmoid'))
      
  
    
    # Setting the optimizer and learning rate
    
    optimizer = hparams[HP_OPTIMIZER]
    learning_rate = hparams[HP_LEARNING_RATE]
    if optimizer == 'adam':
        optimizer = tf.optimizers.Adam(learning_rate = learning_rate)
    elif optimizer == 'sgd':
        optimizer = tf.optimizers.SGD(learning_rate = learning_rate)
    elif optimizer == 'rmsprop':
        optimizer = tf.optimizers.RMSprop(learning_rate = learning_rate)
    else:
        raise ValueError("unexpected optimizer name: %r" %(optimizer_name,))
        
    # compile the model    
    model.compile(optimizer = optimizer, loss='binary_crossentropy',metrics=['accuracy'])
    
    # Fit the model
    early_stop = EarlyStopping(monitor='val_accuracy',patience=10)
    
    history = model.fit(x=training_generator,validation_data=validation_generator,epochs=50,
                       callbacks=[
                           tf.keras.callbacks.TensorBoard(log_dir),
                           hp.KerasCallback(log_dir,hparams),
                           early_stop
                       ])
    return history.history['val_accuracy'][-1],history.history['accuracy'][-1]

DataGenerator produces a batch of X,y,clinical

Is it possible to use X and y an initial input and clinical concatenated to 2nd last Dense layer which is just prior to outpult layer.

Answer 1

Use Functional API:

def create_model(hparams):
    model = Sequential()
        
    model.add(Conv3D(filters=64,kernel_size=(5,5,5),strides=(1,1,1),padding='valid',activation='relu',
                     kernel_regularizer=tf.keras.regularizers.l2(0.000001),input_shape = image_shape))
    #model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='same'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
    
    model.add(Conv3D(filters=128,kernel_size=(3,3,3),strides=(1,1,1),padding='valid',activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='valid'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
        
    model.add(Conv3D(filters=256,kernel_size=(3,3,3),strides=(1,1,1),padding='valid',activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    #model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='same'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
        
    model.add(Conv3D(filters=512,kernel_size=(3,3,3),strides=(1,1,1),padding='valid',activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    model.add(MaxPool3D(pool_size=(3,3,3),strides=(3,3,3),padding='valid'))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
        
    model.add(Flatten())
    
    model.add(Dense(hparams[HP_NUM_UNITS],activation='relu',
                    kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
    model.add(Dropout(hparams[HP_DROPOUT]))
    model.add(BatchNormalization())
    DL = hparams[HP_NUM_DLAYER]
    DU = hparams[HP_NUM_UNITS]
    if DL == 2 and DU == 512:
        model.add(Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
    elif DL == 3 and DU == 512:
        model.add(Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
            
        model.add(Dense(128,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
            
    elif DL == 2 and DU == 1024:
        model.add(Dense(512,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
    else:
        model.add(Dense(512,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
        
        model.add(Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.000001)))
        model.add(Dropout(hparams[HP_DROPOUT]))
        model.add(BatchNormalization())
        
    input = (
      tf.keras.layers.Input(shape=(None,), dtype=tf.float32), # change shape here to your input shape
      tf.keras.layers.Input(shape=(None,), dtype=tf.float32) # change shape here to your input shape
    )
    x = model(input[0])
    x = tf.concat([x, input[1]], 0)
    x = Dense(1,activation='sigmoid'))(x)
    model = tf.keras.Model(inputs=input, outputs=x)

Don't forget to change input shapes.

And you have to change your generator:

    if self.isClinicalData:
        return (X, clinical), y