如何将自定义数据生成器输入 model.fit，它生成 X、y 和一个额外的数组，进入 tensorflow.keras 模型？
How to input custom data generator into model.fit, which generates X,y and one additional array, into tensorflow.keras model?
我正在使用 CNN 解决分类问题。我有患者的 3D 图像（CT 扫描），我试图根据这些图像预测二元结果。我还有一个 临床数据 并想将其包含到 CNN 模型中。我有一个自定义的 *Datagenerator（通过 keras.utils.Sequence），它生成 X、y 以及临床数据数组。
X,y 将在整个模型中使用，并希望在倒数第二个致密层（输出层之前的一层）中添加临床数据
我的数据生成器代码
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
这是我的模特。我正在使用 tensorboard 记录我的指标和超参数
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 生成一批 X,y,clinical
是否可以使用 X 和 y 作为初始输入并临床连接到输出层之前的倒数第二个密集层。
使用函数 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)
别忘了更改输入形状。
而且你必须更换发电机：
if self.isClinicalData:
return (X, clinical), y
我正在使用 CNN 解决分类问题。我有患者的 3D 图像（CT 扫描），我试图根据这些图像预测二元结果。我还有一个 临床数据 并想将其包含到 CNN 模型中。我有一个自定义的 *Datagenerator（通过 keras.utils.Sequence），它生成 X、y 以及临床数据数组。
X,y 将在整个模型中使用，并希望在倒数第二个致密层（输出层之前的一层）中添加临床数据
我的数据生成器代码
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
这是我的模特。我正在使用 tensorboard 记录我的指标和超参数
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 生成一批 X,y,clinical
是否可以使用 X 和 y 作为初始输入并临床连接到输出层之前的倒数第二个密集层。
使用函数 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)
别忘了更改输入形状。
而且你必须更换发电机：
if self.isClinicalData:
return (X, clinical), y