问input_dim参数在Keras多层感知器多隐层情况下的应用

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# To create a Sequential model
model_batch_drop_5lyr = Sequential()

# Hidden Layer1
model_batch_drop_5lyr.add(Dense(684, activation='relu', input_dim = input_dim, kernel_initializer=RandomNormal(mean=0.0, stddev=0.025, seed=None)))
model_batch_drop_5lyr.add(BatchNormalization())
model_batch_drop_5lyr.add(Dropout(0.5)) # 50% no. of neurons droped

# Hidden Layer2
model_batch_drop_5lyr.add(Dense(512, activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.050, seed=None)))
model_batch_drop_5lyr.add(BatchNormalization())
model_batch_drop_5lyr.add(Dropout(0.5)) # 50% no. of neurons droped

# Hidden Layer3
model_batch_drop_5lyr.add(Dense(356, activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.075, seed=None)))
model_batch_drop_5lyr.add(BatchNormalization())
model_batch_drop_5lyr.add(Dropout(0.5)) # 50% no. of neurons droped

# Hidden Layer4
model_batch_drop_5lyr.add(Dense(228, activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.125, seed=None)))
model_batch_drop_5lyr.add(BatchNormalization())
model_batch_drop_5lyr.add(Dropout(0.5)) # 50% no. of neurons droped

# Hidden Layer5
model_batch_drop_5lyr.add(Dense(128, activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.155, seed=None)))
model_batch_drop_5lyr.add(BatchNormalization())
model_batch_drop_5lyr.add(Dropout(0.5)) # 50% no. of neurons droped

# Output Layer
model_batch_drop_5lyr.add(Dense(output_dim, activation='softmax'))

# Printing Summary of the model with 3 hidden layers
print(model_batch_drop_5lyr.summary())