def keras_model2(num_classes, input_dim):
nn_deep_model = OverwrittenSequentialClassifier()
nn_deep_model.add(Dense(2500, input_dim=input_dim, activation='relu'))
nn_deep_model.add(Dense(2000, activation='relu'))
nn_deep_model.add(Dense(1500, activation='relu'))
nn_deep_model.add(Dense(num_classes, activation='softmax'))
model_optimizer = optimizers.Adam(lr=0.001)
nn_deep_model.compile(loss='mean_squared_error', optimizer=model_optimizer, metrics=['accuracy'])
return nn_deep_model
def keras_model_1_lr01(num_classes, input_dim):
model = OverwrittenSequentialClassifier()
model.add(Dense(288, input_dim=input_dim, activation='relu'))
model.add(Dense(144, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(12, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model_optimizer = optimizers.Adam(lr=0.1)
model.compile(loss='mean_squared_error', optimizer=model_optimizer, metrics=['accuracy'])
return model
def keras_model_6_lr1(num_classes, input_dim):
nn_deep_model = OverwrittenSequentialClassifier()
nn_deep_model.add(Dropout(0.7, input_shape=(input_dim,)))
nn_deep_model.add(Dense(1024, activation='relu'))
nn_deep_model.add(Dropout(0.5))
nn_deep_model.add(Dense(num_classes, activation='softmax'))
model_optimizer = optimizers.Adam(lr=1)
nn_deep_model.compile(loss='mean_squared_error', optimizer=model_optimizer, metrics=['accuracy'])
return nn_deep_model