def defineNetwork( self ):
input_tensor = Input( shape = ( self.dim, self.dim,
2 * self.flowSteps ) )
model = BaseModel( input_tensor = input_tensor,
weights = None,
classes = self.classes )
#initial_learning_rate = 1e-2
#lr_schedule = ExponentialDecay( initial_learning_rate,
# decay_steps = 2000,
# decay_rate = 0.96,
# staircase = True )
optimizer = SGD( lr=1e-2, momentum = 0.9, nesterov=True, decay=1e-4 )
model.compile( loss = 'categorical_crossentropy',
optimizer = optimizer,
metrics = [ 'acc' ] )
return model
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D,BatchNormalization
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
from keras import regularizers
InceptionV3 = Sequential()
InceptionV3.add(GlobalAveragePooling2D(input_shape= features_train.shape[1:]))
InceptionV3.add(Dense(128, activation = "relu"))
InceptionV3.add(Dense(37, activation = "softmax"))
InceptionV3.summary()
from keras.callbacks import ModelCheckpoint
InceptionV3.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='weights.best.InceptionV3.hdf5',
verbose=1, save_best_only=True, monitor='val_acc')
InceptionV3.fit(features_train, target_train,
validation_data=(features_valid, target_valid),
epochs=50, batch_size=10, callbacks=[checkpointer], verbose=1)
InceptionV3.load_weights('weights.best.InceptionV3.hdf5')
plant_predictions = [np.argmax(InceptionV3.predict(np.expand_dims(tensor, axis=0))) for tensor in features_test]
# report test accuracy
test_accuracy = 100*np.sum(np.array(plant_predictions)==np.argmax(target_test, axis=1))/len(plant_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
