TypeError：__init __（）缺少1个必需的位置参数：'units'

Question

我正在使用python和张量流，但是我错过了'units'参数，并且我不知道如何解决它，看来您的帖子大部分是代码；请添加更多详细信息。看来您的帖子大部分是代码；请添加更多详细信息。

此处是代码

def createModel():
    model = Sequential()
    # first set of CONV => RELU => MAX POOL layers
    model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=inputShape))
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25)) 
    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
    # returns our fully constructed deep learning + Keras image classifier 
    opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
    # use binary_crossentropy if there are two classes
    model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
    return model

print("Reshaping trainX at..."+ str(datetime.now()))
#print(trainX.sample()) 
print(type(trainX)) # <class 'pandas.core.series.Series'>
print(trainX.shape) # (750,)
from numpy import zeros
Xtrain = np.zeros([trainX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(trainX.shape[0]): # 0 to traindf Size -1
    Xtrain[i] = trainX[i]
print(Xtrain.shape) # (750,128,128,3)
print("Reshaped trainX at..."+ str(datetime.now()))

print("Reshaping valX at..."+ str(datetime.now()))
print(type(valX)) # <class 'pandas.core.series.Series'>
print(valX.shape) # (250,)
from numpy import zeros
Xval = np.zeros([valX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(valX.shape[0]): # 0 to traindf Size -1
    Xval[i] = valX[i]
print(Xval.shape) # (250,128,128,3)
print("Reshaped valX at..."+ str(datetime.now()))

# initialize the model
print("compiling model...")
sys.stdout.flush()
model = createModel()

# print the summary of model
from keras.utils import print_summary
print_summary(model, line_length=None, positions=None, print_fn=None)

# add some visualization
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
SVG(model_to_dot(model).create(prog='dot', format='svg'))

Answer 1

Keras Dense层文档如下：

keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

使用以下内容：

classifier.add(Dense(6, activation='relu', kernel_initializer='glorot_uniform',input_dim=11))

将起作用，因为这里的单位表示output_dim，表示在隐藏层中需要6个神经元。权重使用统一函数进行初始化，并且输入层具有数据集中的11个独立变量（input_dim），以喂入上述隐藏层。

Answer 2

尝试更改此行：

model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))

到

model.add(Dense(NUM_CLASSES, activation='softmax'))

我没有keras的经验，但是在Dense的文档页面上找不到名为output_dim的参数。我想您打算提供单位，但将其标记为output_dim

Answer 3

我认为这是版本问题。在Dense的更新版keras中，没有“ output_dim”参数。

您可以在此文档链接中找到有关Dense参数的信息。

https://keras.io/api/layers/core_layers/dense/

tf.keras.layers.Dense(
    units,
    activation=None,
    use_bias=True,
    kernel_initializer="glorot_uniform",
    bias_initializer="zeros",
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)

第一个参数是“ units”，这是强制性的。

代替此行：

model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))

使用此：

model.add(Dense(units=NUM_CLASSES, activation='softmax'))

或

model.add(Dense(NUM_CLASSES, activation='softmax'))