model.save()
- subclass Model 是不能直接save的,save成.h5,但是能够save_weights,或者save_format="tf"
NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or a Sequential model. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. Consider saving to the Tensorflow SavedModel format (by setting save_format="tf") or using `save_weights`.
model.trainable_variables
- __init__若没有注册该layers,那么在后面应用梯度时会找不到model.trainable_variables。
像下面这样是不行的:
class Map_model(tf.keras.Model):
def __init__(self, is_train=False):
super(Map_model, self).__init__()
def call(self, x):
x = tf.keras.layers.Dense(10, activation='relu')
return x
model.summary()
- 需要先指定input_shape,或者你直接fit一遍它也能自动确定
model.build(input_shape=(None, 448, 448, 3))
print(model.summary())
class Map_model(tf.keras.Model):
def __init__(self, is_train=False):
super(Map_model, self).__init__()
self.map_f1 = tf.keras.layers.Dense(10, activation='relu', trainable=is_train)
# self.map_f2 = tf.keras.layers.Dense(6, activation='relu')
self.map_f3 = tf.keras.layers.Dense(3, activation='softmax', trainable=is_train)
def call(self, x):
x = self.map_f1(x)
# x = self.map_f2(x)
return self.map_f3(x)
@tf.function
def train_step(mmodel, label, L_label, loss_object, train_loss, train_accuracy, optimizer):
with tf.GradientTape() as tape:
L_label_pred = mmodel(label)
loss = loss_object(L_label, L_label_pred)
gradient_l = tape.gradient(loss, mmodel.trainable_variables)
train_loss(loss)
train_accuracy(L_label, L_label_pred)
optimizer.apply_gradients(zip(gradient_l, mmodel.trainable_variables))
def train():
mmodel = Map_model(is_train=True)
optimizer = tf.keras.optimizers.Adam(0.01)
loss_object = tf.keras.losses.CategoricalCrossentropy()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')
EPOCHS = 0
labels = range(1, 30) # labels = truth_label -1
L_labels = [int(prpcs.map2Lclass(l)) for l in labels]
labels = [l - 1 for l in labels]
labels_onehot = tf.one_hot(labels, depth=29)
L_labels_onehot = tf.one_hot(L_labels, depth=3)
EPS = 1e-6
loss_e = 0x7f7f7f
while loss_e > EPS:
EPOCHS += 1
train_loss.reset_states()
train_accuracy.reset_states()
train_step(mmodel, labels_onehot, L_labels_onehot, loss_object, train_loss, train_accuracy, optimizer)
template = 'Epoch {}, Loss: {}, Accuracy: {}'
print(template.format(EPOCHS,
train_loss.result(),
train_accuracy.result() * 100))
loss_e = train_loss.result()
print("labels_onehot shape:", labels_onehot.shape)
model_path = r'./models/'
if not os.path.exists(model_path):
os.makedirs(model_path)
mmodel.save(os.path.join(model_path, 'map_model_{}'.format(EPS)))
mmodel.save_weights(os.path.join(model_path, 'map_model_weights_{}'.format(EPS)))
print("Save model!")
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