FourthBrain

def Generator():
  inputs = tf.keras.layers.Input(shape=[256, 256, 3])

  down_stack = [
    downsample(64, 4, apply_batchnorm=False),  # (bs, 128, 128, 64)
    downsample(128, 4),  # (bs, 64, 64, 128)
    downsample(256, 4),  # (bs, 32, 32, 256)
    downsample(512, 4),  # (bs, 16, 16, 512)
    downsample(512, 4),  # (bs, 8, 8, 512)
    downsample(512, 4),  # (bs, 4, 4, 512)

FourthBrain

class PointNet(nn.Module):
    def __init__(self, classes = 10):
        super().__init__()
        self.transform = Transform()
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, classes)
        

        self.bn1 = nn.BatchNorm1d(512)

FourthBrain

def run_detector(detector, path):
  img = load_img(path)

  converted_img  = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...]
  start_time = time.time()
  result = detector(converted_img)
  end_time = time.time()

  result = {key:value.numpy() for key,value in result.items()}

FourthBrain

class_to_explain = top_pred_classes[0]
simpler_model = LinearRegression()
simpler_model.fit(X=perturbations, y=predictions[:,:,class_to_explain], sample_weight=weights)
coeff = simpler_model.coef_[0]

FourthBrain

from tf_explain.callbacks.occlusion_sensitivity import OcclusionSensitivityCallback
import datetime
%load_ext tensorboard
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
o_callbacks = [OcclusionSensitivityCallback(validation_data=(vis_test, vis_lab),class_index=2,patch_size=4),]
model_TF.compile(optimizer=keras.optimizers.Adam(lr=0.001), loss='binary_crossentropy', metrics=[fbeta])
model_TF.fit(vis_test, vis_lab, epochs=10, verbose=1, callbacks=[tensorboard_callback, o_callbacks])

FourthBrain

# Fully Connected Layer 6                       
model.add(Dropout(0.2))                              # 20% dropout of randomly selected nodes
model.add(Dense(10))                                 # final 10 FCN nodes
model.add(Activation('softmax'))                     # softmax activation
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])