Daniel Haehn PRO
Hi, I am a biomedical imaging and visualization researcher who investigates how the study of brain connectivity and machine perception can help advance the understanding of biologically inspired artificial intelligence.
Assignment 5
PyDicom
load CT volume, slice it, adjust window/level
Thank you!
Assignment 6
Due 4/25!
No class next week!!
Lex Fridman
Artificial General Intelligence
MNIST
Lex Fridman
Supervised Learning
Random Forest in Asst4!
9
Convolutional Neural Network
cat
Convolutional Neural Network
Keras
Easier!
The U-Net
Replace Random Forest with Deep Neural Net!
1. Load Data
2. Setup Network
3. Train Network
4. Predict!
4 Steps
Data
Training
Testing
2
Label
?
Label
But we know the answer!
X_train
y_train
X_test
y_test
Setup Network
NUMBER_OF_CLASSES = 10
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=first_image.shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(NUMBER_OF_CLASSES, activation='softmax'))NUMBER_OF_CLASSES = 10
MNIST
NUMBER_OF_CLASSES = 2Cats vs. Dogs
Setup Network
NUMBER_OF_CLASSES = 10
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=first_image.shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(NUMBER_OF_CLASSES, activation='softmax'))model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])Train Network
9
Training Data
Then we check how well the network predicts the testing data!
?
Loss
should go down!
Repeated.. (1 run is called an epoch)
Predict!
Testing Data
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 6 6
7 7 7
8 8 8
9 9 9
Measure how well the CNN does...
CS480 Lecture 31
By Daniel Haehn
CS480 Lecture 31
Slides for CS80 Biomedical Signal and Image Processing at UMass Boston. See https://cs480.org!
