code stringlengths 3 6.57k |
|---|
callable(int) |
callable(int, ndarray, complex) |
callable(int, ndarray, complex, ndarray) |
for (respectively) |
super() |
initialize(self) |
BaseBackend.initialize() |
self._initialize_mps_mpo() |
copy(self._state) |
compute_step(self) |
copy(self._state) |
self._compute_system_step(next_step, prop_1, prop_2) |
copy(self._state) |
setuptools.find_packages() |
__init__(self,train=True) |
self.build_model() |
self.train(self.model) |
self.model.load_weights('cifar10vgg.h5') |
build_model(self) |
Sequential() |
model.add(Conv2D(64, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.3) |
model.add(Conv2D(64, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Conv2D(128, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(128, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Conv2D(256, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(256, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(256, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Conv2D(512, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(512, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(512, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Conv2D(512, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(512, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.4) |
model.add(Conv2D(512, (3, 3) |
regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Dropout(0.5) |
model.add(Flatten() |
model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay) |
model.add(Activation('relu') |
model.add(BatchNormalization() |
model.add(Dropout(0.5) |
model.add(Dense(self.num_classes) |
model.add(Activation('softmax') |
normalize(self,X_train,X_test) |
np.mean(X_train,axis=(0,1,2,3) |
np.std(X_train, axis=(0, 1, 2, 3) |
normalize_production(self,x) |
return (x-mean) |
predict(self,x,normalize=True,batch_size=50) |
self.normalize_production(x) |
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