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more cov: 1.0654/0.6827 -> 0.70131
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orbxball committed Apr 17, 2017
1 parent 8002585 commit 8befacb
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Showing 2 changed files with 10 additions and 10 deletions.
2 changes: 1 addition & 1 deletion hw3/test_cnn.py
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ def ensure_dir(file_path):
height = width = 48
num_classes = 7
input_shape = (height, width, 1)
model_name = 'pre4.h5'
model_name = 'pre5.h5'

# Read the test data
with open(sys.argv[1], "r+") as f:
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18 changes: 9 additions & 9 deletions hw3/train_cnn.py
Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,9 @@
num_classes = 7
input_shape = (height, width, 1)
batch_size = 128
epochs = 30
epochs = 100
zoom_range = 0.05
model_name = 'pre4.h5'
model_name = 'pre5.h5'
isValid = 1

# Read the train data
Expand Down Expand Up @@ -50,32 +50,32 @@
model.add(LeakyReLU(alpha=0.03))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(0.3))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(LeakyReLU(alpha=0.03))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(0.3))

model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Conv2D(256, (3, 3), padding='same'))
model.add(LeakyReLU(alpha=0.03))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(0.3))

model.add(Conv2D(256, (3, 3), padding='same'))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(LeakyReLU(alpha=0.03))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Dropout(0.3))
model.add(Dropout(0.4))

model.add(Flatten())

model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(num_classes))
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