Open aynesss opened 2 years ago
Hello, I'm trying to load a r50_1x_sk0/saved_model of pretrained checkpoint-tf2 of simCLRv2, to just fine tuned but I'm coming across this error: '_UserObject' object has no attribute 'summary' I would to apply
.summary()
.fit()
and.predict()
Here is a part of my codedef create_model(path): baseModel = tf.saved_model.load(path) baseModel.summary() model_output = tf.keras.layers.Dense(3, name="head_supervised_new")(baseModel) model = tf.keras.Model(inputs=baseModel, outputs=model_output) model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001), loss=tf.keras.losses.CategoricalCrossentropy(), metrics=[tf.keras.metrics.CategoricalAccuracy()]) return model loaded_model = create_model("gs://simclr-checkpoints-tf2/simclrv2/pretrained/r50_2x_sk1/saved_model") loaded_model.fit(training_set)
I have the same issue.
I was having the same problem but it turns out loaded_model is a User_Object, the actual model is located at loaded_model.model
.
So for example try loaded_model.model.summary()
I was having the same problem but it turns out loaded_model is a User_Object, the actual model is located at
loaded_model.model
. So for example tryloaded_model.model.summary()
I think this only works with
import tensorflow as tf
model = tf.keras.models.load_model()
model.model.summary()
If you use
model = tf.saved_model.load()
you will get a UserObject again after accessing model.model
Did anyone get the model to work with keras methods?
I trying to access the layers e.g. the projection_head
but get AttributeError: Layer projection_head has no inbound nodes.
after:
import tensorflow as tf
saved_model_path = 'gs://simclr-checkpoints-tf2/simclrv2/pretrained/r50_1x_sk0/saved_model/'
model = tf.keras.models.load_model(saved_model_path).model
model.get_layer('projection_head')
Hello, I'm trying to load a r50_1x_sk0/saved_model of pretrained checkpoint-tf2 of simCLRv2, to just fine tuned but I'm coming across this error: '_UserObject' object has no attribute 'summary' I would to apply
.summary()
.fit()
and.predict()
Here is a part of my code