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# random seed | |
random_seed = 3 | |
# Split | |
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.2, random_state=random_seed) |
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model = Sequential() | |
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', | |
activation ='relu', input_shape = (28,28,1))) | |
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', | |
activation ='relu')) | |
model.add(MaxPool2D(pool_size=(2,2))) | |
model.add(Dropout(0.25)) | |
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', |
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# Optimizer | |
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) | |
# Compile the model | |
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) | |
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# Set LR metric monitor | |
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', | |
patience=3, | |
verbose=1, | |
factor=0.4, | |
min_lr=0.00001) | |
epochs = 500 | |
batch_size = 32 #to start with |
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datagen = ImageDataGenerator( | |
featurewise_center=False, | |
samplewise_center=False, | |
featurewise_std_normalization=False, | |
samplewise_std_normalization=False, | |
zca_whitening=False, | |
rotation_range=10, # randomly rotate images | |
zoom_range = 0.2, # Randomly zoom image | |
width_shift_range=0.2, # randomly shift images horizontally | |
height_shift_range=0.2, # randomly shift images vertically |
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# prediction | |
results = model.predict(test) | |
# Index with the maximum probability | |
results = np.argmax(results,axis = 1) | |
results = pd.Series(results,name="Label") |
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