Keras 的 MSE 损失函数显示与 Tensorflow 的 MSE 度量不同的输出？

Question

I am training a convolutional network with continuous output in the last layer. The last layer has 4 nodes. I am using the Mean Squared Error as a loss function. As a check I used the Mean Squared Error from Tensorflow. This gave only the same results for the first batch of the first epoch. Therefore my question is why do these differ? I used convolutional layers with max pooling and in the end I flattened it and used dropout.

Moreover, I was also wondering how is the Mean Squared Error computed for 4 nodes? Is it just summing the Mean Squared Error of each node? Cause when I calculate the Mean Squared Error per node there is not a clear connection.

This is the metric.

def loss(y_true, y_pred):
    loss = tf.metrics.mean_squared_error(y_true, y_pred)[1]
    K.get_session().run(tf.local_variables_initializer())
    return loss

And here I compile the model

model.compile(loss='mse', optimizer= adam, metrics=[loss, node])

This is how I calculated the Mean squared Error for one node:

def node(y_true, y_pred):
    loss = tf.metrics.mean_squared_error(y_true[:,0], y_pred[:,0])[1]
    K.get_session().run(tf.local_variables_initializer())
    return node

And this is a simplified form of the model:

    width = height = 128
    model = Sequential()

    model.add(Convolution2D(filters=64, kernel_size=(5, 5), activation='relu', padding='same',
                            input_shape=(width, height, 1)))
     model.add(MaxPooling2D(pool_size=(3, 3)))
    model.add(Flatten())
    model.add(Dense(units=256, activation='relu'))
    model.add(Dropout(0.4))
    model.add(Dense(units=4, activation='linear'))

    adam = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0) 
    model.compile(loss='mse', optimizer= adam, metrics=[loss,node])

Answer 1

You are returning the function itself.

Look at your code:

def node(y_true, y_pred):
    loss = tf.metrics.mean_squared_error(y_true[:,0], y_pred[:,0])[1]
    K.get_session().run(tf.local_variables_initializer())
    return node # This is a function name. This should be "return loss"

Try correcting this first.