I an stacking two models trained on different inputs from two data collections as shown below using Tensorflow Keras 2.6.2. The stacking is performed with a convolutional meta-learner to predict on a common hold out test set. Given below is the code and he model architecture.
#load data
#datase-1
X_tr1 = np.load('data/X_tr1.npy') #shape (200, 224,224,3)
Y_tr1 = np.load('data/Y_tr1.npy') #shape (200, 224,224,1)
X_val1 = np.load('data/X_val1.npy') #shape (100, 224,224,3)
Y_val1 = np.load('data/Y_val1.npy') #shape (100, 224,224,1)
#dataset-2
X_tr2 = np.load('data/X_tr2.npy') #shape (200, 224,224,3)
Y_tr2 = np.load('data/Y_tr2.npy') #shape (200, 224,224,1)
X_val2 = np.load('data/X_val2.npy') #shape (100, 224,224,3)
Y_val2 = np.load('data/Y_val2.npy') #shape (100, 224,224,1)
#common hold-out test set
X_ts = np.load('data/X_ts.npy') #shape (50, 224,224,3)
Y_ts = np.load('data/Y_ts.npy') #shape (50, 224,224,1)
#%%
#instantiate the models
img_width, img_height = 224,224
input_shape = (img_width, img_height, 3) #RGB inputs
model_input1 = Input(shape=input_shape) #input to model1
model_input2 = Input(shape=input_shape) #input to model2
n_classes=1 #grayscale mask output
activation='sigmoid'
batch_size = 8
n_epochs = 256
BACKBONE = 'vgg16'
# define model
model1 = sm.Unet(BACKBONE, encoder_weights='imagenet',
classes=n_classes, activation=activation)
model2 = sm.Unet(BACKBONE, encoder_weights='imagenet',
classes=n_classes, activation=activation)
#%%
# constructing a stacking ensemble of the two models
# A second-level fully-convolutional meta-learner is used to learn
# the features extracted from the penultimate layers of the models
n_models = 2
def load_all_models(n_models):
all_models = list()
model1.load_weights('weights/vgg16_1.hdf5') # path to model1
model_loss1a=Model(inputs=model1.input,
outputs=model1.get_layer('decoder_stage4b_relu').output) #name of the penultimate layer
x1 = model_loss1a.output
model1a = Model(inputs=model1.input, outputs=x1, name='model1')
all_models.append(model1a)
model2.load_weights('weights/vgg16_2.hdf5') #path to model2
model_loss2a=Model(inputs=model2.input,
outputs=model2.get_layer('decoder_stage4b_relu').output)
x2 = model_loss2a.output
model2a = Model(inputs=model2.input, outputs=x2, name='model2')
all_models.append(model2a)
return all_models
# load models
n_members = 2
members = load_all_models(n_members)
print('Loaded %d models' % len(members))
def define_stacked_model(members):
# update all layers in all models to not be trainable
for i in range(len(members)):
model = members[i]
for layer in model.layers [1:]:
# make not trainable
layer.trainable = False
layer._name = 'ensemble_' str(i 1) '_' layer.name
ensemble_outputs = [model(model_input1, model_input2) for model in members]
merge = Concatenate()(ensemble_outputs)
# meta-learner, fully-convolutional
x4 = Conv2D(128, (3,3), activation='relu',
name = 'NewConv1', padding='same')(merge)
x5 = Conv2D(1, (1,1), activation='sigmoid',
name = 'NewConvfinal')(x4)
model= Model(inputs=[model_input1,model_input2],
outputs=x4)
return model
print("Creating Ensemble")
ensemble = define_stacked_model(members)
print("Ensemble architecture: ")
print(ensemble.summary())
Shown below is the architecture of the stacked model:
Model: "model_4"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
model1 (Functional) (None, None, None, 1 23752128 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
model2 (Functional) (None, None, None, 1 23752128 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 224, 224, 32) 0 model1[0][0]
model2[0][0]
__________________________________________________________________________________________________
NewConv1 (Conv2D) (None, 224, 224, 128 36992 concatenate[0][0]
__________________________________________________________________________________________________
NewConv2 (Conv2D) (None, 224, 224, 64) 73792 NewConv1[0][0]
__________________________________________________________________________________________________
NewConv3 (Conv2D) (None, 224, 224, 32) 18464 NewConv2[0][0]
__________________________________________________________________________________________________
NewConvfinal (Conv2D) (None, 224, 224, 1) 33 NewConv3[0][0]
==================================================================================================
Total params: 47,633,537
Trainable params: 129,281
Non-trainable params: 47,504,256
I compile and train the model as shown below:
opt = keras.optimizers.Adam(lr=0.001)
loss_func='binary_crossentropy'
ensemble.compile(optimizer=opt,
loss=loss_func,
metrics=['binary_accuracy'])
results_ensemble = ensemble.fit((X_tr1, Y_tr1, X_tr2, Y_tr2),
batch_size=batch_size,
epochs=n_epochs,
verbose=1,
validation_data=(X_val1, Y_val1, X_val2, Y_val2))
I get the following error:
Traceback (most recent call last):
File "/home/codes/untitled5.py", line 563, in <module>
validation_data=(X_val1, Y_val1, X_val2, Y_val2))
File "/home/anaconda3/envs/tf262/lib/python3.7/site-packages/keras/engine/training.py", line 1125, in fit
data_adapter.unpack_x_y_sample_weight(validation_data))
File "/home/anaconda3/envs/tf262/lib/python3.7/site-packages/keras/engine/data_adapter.py", line 1574, in unpack_x_y_sample_weight
raise ValueError(error_msg)
ValueError: Data is expected to be in format `x`, `(x,)`, `(x, y)`, or `(x, y, sample_weight)`, found: (array([[[[0.09803922, 0.09803922, 0.09803922],
[0.09803922, 0.09803922, 0.09803922],
[0.09803922, 0.09803922, 0.09803922],
...,
[0.08627451, 0.08627451, 0.08627451],
[0.08627451, 0.08627451, 0.08627451],
[0.05098039, 0.05098039, 0.05098039]],...
Also how do I predict with a single X_ts provided the ensemble model now has two separate inputs?
New error after trying to implement the suggestions:
File "/home/codes/untitled5.py", line 595, in <module>
validation_data=outputs)
File "/home/anaconda3/envs/tf262/lib/python3.7/site-packages/keras/engine/training.py", line 1184, in fit
tmp_logs = self.train_function(iterator)
ValueError: Layer model_4 expects 2 input(s), but it received 4 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 224, 224, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 224, 224, 1) dtype=float32>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 224, 224, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 224, 224, 1) dtype=float32>]
CodePudding user response：
Answer based on comment. Multi-inputs need to be passed as a list, not a tuple.
Change:
results_ensemble = ensemble.fit((X_tr1, Y_tr1, X_tr2, Y_tr2),
batch_size=batch_size,
epochs=n_epochs,
verbose=1,
validation_data=(X_val1, Y_val1, X_val2, Y_val2))
To:
inputs = [X_tr1, Y_tr1, X_tr2, Y_tr2] # you can pass the list itself or the variable
results_ensemble = ensemble.fit(inputs,
batch_size=batch_size,
epochs=n_epochs,
verbose=1,
validation_data=([X_val1, X_val2], y_val))
# test_inputs_diff = [x_test1, x_test2] # different input
# test_inputs_same = [x_test1, x_test1] # same input
# preds_diff = ensemble.predict(test_inputs_diff)
# preds_same = ensemble.predict(test_inputs_same)