Exercise 12.3¶
Discriminative localization (CAM)¶
In this exercise we will create class activation maps (CAMs) for predictions made by a model trained to classify magentic phases (see Exercise 7_1).
- Pick out two correctly and two wrongly classified images classified with a convolutional network.
- Look at Exercise 8.1 to extract weights and feature maps from the trained network model.
- Create and plot the class activation maps and compare them with the images in order to see which regions lead to the prediction.
In [3]:
from tensorflow import keras
import numpy as np
callbacks = keras.callbacks
layers = keras.layers
keras version 2.4.0
Load and prepare dataset¶
See https://doi.org/10.1038/nphys4035 for more information
In [4]:
import gdown
url = "https://drive.google.com/u/0/uc?export=download&confirm=HgGH&id=1Ihxt1hb3Kyv0IrjHlsYb9x9QY7l7n2Sl"
output = 'ising_data.npz'
gdown.download(url, output, quiet=True)
f = np.load(output)
n_train = 20000
x_train, x_test = f["C"][:n_train], f["C"][n_train:]
T_train, T_test = f["T"][:n_train], f["T"][n_train:]
Tc = 2.27
y_train = np.zeros_like(T_train)
y_train[T_train > Tc] = 1
y_train = keras.utils.to_categorical(y_train, 2)
y_test = np.zeros_like(T_test)
y_test[T_test > Tc] = 1
y_test = keras.utils.to_categorical(y_test, 2)
Plot data¶
In [5]:
import matplotlib.pyplot as plt
for i,j in enumerate(np.random.choice(n_train, 6)):
plt.subplot(2,3,i+1)
image = x_train[j]
plot = plt.imshow(image)
plt.title("T: %.2f" % T_train[j])
plt.tight_layout()
plt.show()
Definition of the model¶
Define a CNN for discriminative localization. Note that the CNN must use GlobalAveragePooling2D after the convolutional part and must not feature more than a single fully-connected layer as output.
In [6]:
model = keras.models.Sequential()
model.add(layers.InputLayer(input_shape=(32, 32)))
model.add(layers.Reshape((32, 32,1)))
model.add(layers.Convolution2D(16, (3, 3), padding='same', activation='relu'))
model.add(layers.Convolution2D(16, (3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Convolution2D(32, (3, 3), padding='same', activation='relu'))
model.add(layers.Convolution2D(32, (3, 3), padding='same', activation='relu'))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dropout(0.25))
model.add(layers.Dense(2, activation='softmax'))
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= reshape (Reshape) (None, 32, 32, 1) 0 _________________________________________________________________ conv2d (Conv2D) (None, 32, 32, 16) 160 _________________________________________________________________ conv2d_1 (Conv2D) (None, 32, 32, 16) 2320 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 16, 16, 16) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 16, 16, 32) 4640 _________________________________________________________________ conv2d_3 (Conv2D) (None, 16, 16, 32) 9248 _________________________________________________________________ global_average_pooling2d (Gl (None, 32) 0 _________________________________________________________________ dropout (Dropout) (None, 32) 0 _________________________________________________________________ dense (Dense) (None, 2) 66 ================================================================= Total params: 16,434 Trainable params: 16,434 Non-trainable params: 0 _________________________________________________________________
prepare model for training¶
In [8]:
model.compile(
loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(0.001),
metrics=['accuracy'])
In [9]:
results = model.fit(x_train, y_train,
batch_size=64,
epochs=50,
verbose=2,
validation_split=0.1,
callbacks=[
callbacks.EarlyStopping(patience=5, verbose=1),
callbacks.ReduceLROnPlateau(factor=0.67, patience=2, verbose=1)]
)
Epoch 1/50 282/282 - 10s - loss: 0.1006 - accuracy: 0.9611 - val_loss: 0.0612 - val_accuracy: 0.9750 Epoch 2/50 282/282 - 14s - loss: 0.0476 - accuracy: 0.9814 - val_loss: 0.0415 - val_accuracy: 0.9825 Epoch 3/50 282/282 - 23s - loss: 0.0464 - accuracy: 0.9810 - val_loss: 0.0555 - val_accuracy: 0.9785 Epoch 4/50 282/282 - 24s - loss: 0.0480 - accuracy: 0.9804 - val_loss: 0.0364 - val_accuracy: 0.9845 Epoch 5/50 282/282 - 24s - loss: 0.0465 - accuracy: 0.9809 - val_loss: 0.0379 - val_accuracy: 0.9840 Epoch 6/50 282/282 - 25s - loss: 0.0443 - accuracy: 0.9811 - val_loss: 0.0435 - val_accuracy: 0.9835 Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0006700000318232924. Epoch 7/50 282/282 - 23s - loss: 0.0421 - accuracy: 0.9832 - val_loss: 0.0377 - val_accuracy: 0.9840 Epoch 8/50 282/282 - 25s - loss: 0.0424 - accuracy: 0.9823 - val_loss: 0.0388 - val_accuracy: 0.9835 Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0004489000252215192. Epoch 9/50 282/282 - 25s - loss: 0.0396 - accuracy: 0.9828 - val_loss: 0.0390 - val_accuracy: 0.9835 Epoch 00009: early stopping
Evaluate training¶
In [10]:
plt.figure(1, (12, 4))
plt.subplot(1, 2, 1)
plt.plot(results.history['loss'])
plt.plot(results.history['val_loss'])
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper right')
Out[10]:
<matplotlib.legend.Legend at 0x7ff7a4313d30>
In [11]:
plt.figure(1, (12, 4))
plt.subplot(1, 2, 1)
plt.plot(results.history['accuracy'])
plt.plot(results.history['val_accuracy'])
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper right')
Out[11]:
<matplotlib.legend.Legend at 0x7ff7a429b668>
Create class activation maps¶
Task¶
Look at Exercise 8.1 to extract weights and feature maps from the trained network model.
First, extract the activations of the last convolutional layer.
In [ ]:
conv = model.layers[-4] # last conv layer
Then, create the class activation maps by omitting the global average pooling operation and applying the weights of the single classification layer to the extracted activations.
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Plot the class activation maps for examples just below and above the critical temperature¶
Task¶
Plot the CAMs for wrongly and correctly classified images.
Note, you can use interpolation='bilinear in plt.imshow to upsample the CAMs.
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