我想查看训练 tf.keras 模型的最终输出。在这种情况下，它将是来自softmax函数的预测数组，例如[0,0,0,1,0,1]。
这里的其他线程建议使用 model.predict(training_data(，但这不适用于我的情况，因为我在训练和验证中使用 dropout，因此神经元是随机丢弃的，使用相同的数据再次预测将给出不同的结果。
def get_model():
inputs = tf.keras.layers.Input(shape=(input_dims,))
x = tf.keras.layers.Dropout(rate=dropout_rate)(inputs, training=True)
x = tf.keras.layers.Dense(units=29, activation='relu')(x)
x = tf.keras.layers.Dropout(rate=dropout_rate)(x, training=True)
x = tf.keras.layers.Dense(units=15, activation='relu')(x)
outputs = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
return model
myModel = get_model()
myModel.summary()
myModel.fit(X_train, y_train,
batch_size = batch_size,
epochs= epochs,
verbose = 1,
validation_data = (X_val, y_val))
在 tensorflow 中，您可以在训练后非常轻松地获取模型的输出。下面是来自 Github 存储库的示例：
input = tf.placeholder(tf.float32, shape=[None, INPUT_DIMS])
labels = tf.placeholder(tf.float32, shape=[None])
hidden = tf.nn.tanh(make_nn_layer(normalized, NUM_HIDDEN))
logits = make_nn_layer(hidden, NUM_CLASSES)
outputs = tf.argmax(logits, 1)
int_labels = tf.to_int64(labels)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, int_labels, name='xentropy')
train_step = tf.train.AdamOptimizer().minimize(cross_entropy)
correct_prediction = tf.equal(outputs, int_labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
validation_dict = {
input: validation_data[:,0:7],
labels: validation_data[:,7],}
for i in range(NUM_BATCHES):
batch = training_data[numpy.random.choice(training_size, BATCH_SIZE, False),:]
train_step.run({input: batch[:,0:7], labels: batch[:,7]})
if i % 100 == 0 or i == NUM_BATCHES - 1:
print('Accuracy %.2f%% at step %d' % (accuracy.eval(validation_dict) * 100, i))
output_data = outputs.eval({input: data_vector[:,0:7]})
我能从训练的模型获得的唯一输出似乎是历史对象。还有一个myModel.output对象，但它是一个张量，如果不将数据放入其中，我就无法评估它。有什么想法吗？
据我所知，在调用层时，您无法在传递training=True后关闭dropout(除非您将权重转移到具有相同架构的新模型(。但是，相反，您可以在正常情况下构建和训练模型(即在调用中不使用training参数(，然后通过定义后端函数(即keras.backend.function()(并设置学习阶段(即keras.backend.learning_phase()(：
# build your model normally (i.e. without using `training=True` argument)
# train your model...
from keras import backend as K
func = K.function(model.inputs + [K.learning_phase()], model.outputs)
# run the model with dropout layers being active, i.e. learning_phase == 1
preds = func(list_of_input_arrays + [1])
# run the model with dropout layers being inactive, i.e. learning_phase == 0
preds = func(list_of_input_arrays + [0])
更新：正如我上面建议的，另一种方法是定义一个具有相同架构但不设置training=True的新模型，然后将权重从训练模型转移到这个新模型。为此，我只需在get_model()函数中添加一个training参数：
def get_model(training=None):
inputs = tf.keras.layers.Input(shape=(input_dims,))
x = tf.keras.layers.Dropout(rate=dropout_rate)(inputs, training=training)
x = tf.keras.layers.Dense(units=29, activation='relu')(x)
x = tf.keras.layers.Dropout(rate=dropout_rate)(x, training=training)
x = tf.keras.layers.Dense(units=15, activation='relu')(x)
outputs = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
return model
# build a model with dropout layers active in both training and test phases
myModel = get_model(training=True)
# train the model
myModel.fit(...)
# build a clone of the model with dropouts deactivated in test phase
myTestModel = get_model() # note: the `training` is `None` by default
# transfer the weights from the trained model to this model
myTestModel.set_weights(myModel.get_weights())
# use the new model in test phase; the dropouts would not be active
myTestModel.predict(...)