keras fit vs keras评价

应该有人能够真正澄清这个......

以下是Keras文档中的一些初始信息：Keras中的 fit 函数只是为给定数量的时期训练模型 . evaluate 函数返回测试模式下模型的损失值和度量值 .

因此，这两个函数都会带来损失 . 为了给出一个例子，如果我有一个单一的训练例子，我在每个训练步骤后从fit函数得到的损失应该与我从evaluate函数得到的损失相同（在相同的训练步骤之后） . （这里的假设是我在同一列车组上运行 fit 和 evaluate 函数（仅包含1个示例） . ）

我将我的网络定义如下：

def identity_loss(y_true, y_pred):
    return K.mean(y_pred - 0 * y_true)

model = ResNet50(weights='imagenet')
model.layers.pop()
x = model.get_layer('flatten_1').output # layer 'flatten_1' is the last layer of the model
model_out = Dense(128, activation='relu',  name='model_out')(x)
model_out = Lambda(lambda  x: K.l2_normalize(x,axis=-1))(model_out)

new_model = Model(inputs=model.input, outputs=model_out)

anchor_input = Input(shape=(224, 224, 3), name='anchor_input')
pos_input = Input(shape=(224, 224, 3), name='pos_input')
neg_input = Input(shape=(224, 224, 3), name='neg_input')

encoding_anchor   = new_model(anchor_input)
encoding_pos      = new_model(pos_input)
encoding_neg      = new_model(neg_input)

loss = Lambda(triplet_loss)([encoding_anchor, encoding_pos, encoding_neg])
siamese_network = Model(inputs  = [anchor_input, pos_input, neg_input], 
                        outputs = loss) 
siamese_network.compile(loss=identity_loss, optimizer=Adam(lr=.00003))

稍后，我训练我的火车组（仅包括1个示例）和10个时期的拟合函数 . 只是为了检查fit和evaluate函数之间的差异，我还在每个时期的fit函数之后运行evaluate函数，输出看起来像下面的：

nr_epoch:  0 

Epoch 1/1
1/1 [==============================] - 4s 4s/step - loss: 2.0035
1/1 [==============================] - 3s 3s/step
eval_score for train set:  2.0027356147766113

nr_epoch:  1 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.9816
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.001833915710449

nr_epoch:  2 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.9601
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.00126576423645

nr_epoch:  3 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.9388
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.0009117126464844

nr_epoch:  4 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.9176
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.000725746154785

nr_epoch:  5 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.8964
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.0006520748138428

nr_epoch:  6 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.8759
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.0006656646728516

nr_epoch:  7 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.8555
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.0007567405700684

nr_epoch:  8 

Epoch 1/1
1/1 [==============================] - 1s 1s/step - loss: 1.8355
1/1 [==============================] - 1s 1s/step
eval_score for train set:  2.0009000301361084

nr_epoch:  9 

Epoch 1/1
1/1 [==============================] - 2s 2s/step - loss: 1.8159
1/1 [==============================] - 2s 2s/step
eval_score for train set:  2.001085042953491

如图所示， fit 函数（在eachepoch结尾处）报告的 loss 正在减少 . 而来自评估函数的损失并没有减少 .

所以困境是：如果我在一个单一训练样例上运行我的模型，我是否应该从同一时期的拟合和评估函数中看到相同的损失（在每个时期之后）？如果我继续训练，列车损失正在减少，但来自评估功能的损失会以某种方式保持在同一水平并且不会减少

最后，这是我称之为适合和评估函数的方式：

z = np.zeros(len(anchor_path))

siamese_network.fit(x=[anchor_imgs, pos_imgs, neg_imgs], 
                    y=z, 
                    batch_size=batch_size, 
                    epochs=1, 
                    verbose=1, 
                    callbacks=None, 
                    validation_split=0.0, 
                    validation_data=None, 
                    shuffle=True, 
                    class_weight=None, 
                    sample_weight=None, 
                    initial_epoch=0, 
                    steps_per_epoch=None, 
                    validation_steps=None)

eval_score = siamese_network.evaluate(x=[anchor_imgs, pos_imgs, neg_imgs], 
                                      y=z,
                                      batch_size = batch_size, 
                                      verbose = 1)
print('eval_score for train set: ', eval_score)

那么，为什么在执行拟合函数期间损失会减少而不是评估函数呢？我在哪里弄错了？