The topic "Fable pets mgisu link" seems to be a bit confusing as it combines elements from different areas. However, let's break it down and try to provide some context. Fables are traditional stories, often with a moral lesson, that involve talking animals or mythical creatures. These stories can be found in various cultures and are passed down through generations. Fables are known for their simplicity and the use of animals as characters to teach lessons or convey messages. Pets, on the other hand, are domesticated animals that are kept for companionship or enjoyment.
Pets, on the other hand, are domesticated animals that are kept for companionship or enjoyment. They can be dogs, cats, birds, or many other types of animals. Pets are often considered as part of the family and bring joy, comfort, and companionship to their owners.
How to load a trained TF1 protobuf model into TF2?
Update: This is a bug in tensorflow. Track progress here. I have created and trained a model using stable-baselines, which uses Tensorflow 1. Now I need to use this trained model in an environment where I only have access to Tensorflow 2 or PyTorch. I figured I would go with Tensorflow 2 as the documentation says I should be able to load models created with Tensorflow 1. I can load the pb file without a problem in Tensorflow 1:
global_session = tf.Session() with global_session.as_default(): model_loaded = tf.saved_model.load_v2('tensorflow_model') model_loaded = model_loaded.signatures['serving_default'] init = tf.global_variables_initializer() global_session.run(init) can_be_imported = tf.saved_model.contains_saved_model('tensorflow_model') assert(can_be_imported) model_loaded = tf.saved_model.load('tensorflow_model/') ValueError: Node 'loss/gradients/model/batch_normalization_3/FusedBatchNormV3_1_grad/FusedBatchNormGradV3' has an _output_shapes attribute inconsistent with the GraphDef for output #3: Dimension 0 in both shapes must be equal, but are 0 and 64. Shapes are [0] and [64]. NUM_CHANNELS = 64 BN1 = BatchNormalization() BN2 = BatchNormalization() BN3 = BatchNormalization() BN4 = BatchNormalization() BN5 = BatchNormalization() BN6 = BatchNormalization() CONV1 = Conv2D(NUM_CHANNELS, kernel_size=3, strides=1, padding='same') CONV2 = Conv2D(NUM_CHANNELS, kernel_size=3, strides=1, padding='same') CONV3 = Conv2D(NUM_CHANNELS, kernel_size=3, strides=1) CONV4 = Conv2D(NUM_CHANNELS, kernel_size=3, strides=1) FC1 = Dense(128) FC2 = Dense(64) FC3 = Dense(7) def modified_cnn(inputs, **kwargs): relu = tf.nn.relu log_softmax = tf.nn.log_softmax layer_1_out = relu(BN1(CONV1(inputs))) layer_2_out = relu(BN2(CONV2(layer_1_out))) layer_3_out = relu(BN3(CONV3(layer_2_out))) layer_4_out = relu(BN4(CONV4(layer_3_out))) flattened = tf.reshape(layer_4_out, [-1, NUM_CHANNELS * 3 * 2]) layer_5_out = relu(BN5(FC1(flattened))) layer_6_out = relu(BN6(FC2(layer_5_out))) return log_softmax(FC3(layer_6_out)) class CustomCnnPolicy(CnnPolicy): def __init__(self, *args, **kwargs): super(CustomCnnPolicy, self).__init__(*args, **kwargs, cnn_extractor=modified_cnn) model = PPO2(CustomCnnPolicy, env, verbose=1) with model.graph.as_default(): tf.saved_model.simple_save(model.sess, 'tensorflow_model', inputs=, outputs=) Fully reproducible code can be found in the following 2 google colab notebooks: Tensorflow 1 saving and loading Tensorflow 2 loading Direct link to the saved model: model
- tensorflow
- tensorflow2.0
- stable-baselines
Hello everyone, I’ve let this blog rot away in this corner of the internet.
"Mgisu" seems to be a misspelling or a specific term that is not widely known. Without further information or context, it is difficult to give a precise interpretation or meaning to it. Lastly, "link" can refer to a connection or relationship between two things. Perhaps there is a connection between fables and pets in some context, but it is unclear what exactly that might be without more information. In conclusion, the topic "Fable pets mgisu link" is not clear and seems to combine concepts from different areas without a clear connection. Without further information or clarification, it is challenging to provide a more detailed discussion on this topic..
Reviews for "The Hidden Powers of Fable Pets and the MGISU Connection"
1. John - 2 Stars - I was really disappointed with Fable Pets mgisu link. The graphics were subpar and the gameplay was boring. There wasn't much to do other than feed and clean up after the pets. Overall, I found the game to be unengaging and not worth my time.
2. Sarah - 1 Star - Fable Pets mgisu link was a huge letdown. The pets were not cute at all and there weren't many options for customization. The tasks were repetitive and there wasn't a clear objective or storyline to follow. I quickly got bored and uninstalled the game after just a few minutes of playing.
3. Alex - 2 Stars - I found Fable Pets mgisu link to be lacking in content. There were only a few different pets to choose from and the interactions with them were limited. The game felt unfinished and lacked depth. I was really hoping for more engaging gameplay and variety, but unfortunately, it didn't deliver.
4. Emily - 1 Star - I found Fable Pets mgisu link to be incredibly frustrating. The controls were clunky and it was difficult to navigate through the menus. The game kept crashing and I had to constantly restart it. It was a frustrating experience overall and I would not recommend it to others.
5. Michael - 2 Stars - Fable Pets mgisu link had potential, but it fell short in execution. The visuals were outdated and the animations were mediocre at best. The gameplay was repetitive and there wasn't much to do beyond the basic tasks. I quickly lost interest and found myself looking for a more engaging game to play.