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| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras.datasets import mnist | |
| from tensorflow.keras.utils import to_categorical | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten | |
| from tensorflow.keras.optimizers import RMSprop | |
| # download the mnist to the path '~/.keras/datasets/' if it is the first time to be called | |
| # X shape (60,000 28x28), y shape (10,000, ) | |
| (x_train, y_train), (x_test, y_test) = mnist.load_data() | |
| # data pre-processing | |
| x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1) / 255. # normalize | |
| x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1) / 255. # normalize | |
| y_train = to_categorical(y_train, num_classes=10) #one hot | |
| y_test = to_categorical(y_test, num_classes=10) #one hot | |
| # Create model | |
| model = Sequential() | |
| model.add(Conv2D(16, (3, 3), input_shape=(28, 28, 1))) | |
| model.add(Activation('relu')) | |
| model.add(Flatten()) | |
| model.add(Dense(256)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(10)) | |
| model.add(Activation('softmax', name='pred')) | |
| # Quantization aware training | |
| sess = tf.keras.backend.get_session() | |
| tf.contrib.quantize.create_training_graph(sess.graph) | |
| sess.run(tf.global_variables_initializer()) | |
| # You can plot the quantize training graph on tensorboard | |
| # tf.summary.FileWriter('/workspace/tensorboard', graph=sess.graph) | |
| # Define optimizer | |
| rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) | |
| # We add metrics to get more results you want to see | |
| model.compile(optimizer=rmsprop, | |
| loss='categorical_crossentropy', | |
| metrics=['accuracy']) | |
| model.fit(x_train, y_train, epochs=1, batch_size=256) | |
| print('\nTesting ------------') | |
| # Evaluate the model with the metrics we defined earlier | |
| loss, accuracy = model.evaluate(x_test, y_test) | |
| print('test loss: ', loss) | |
| print('test accuracy: ', accuracy) | |
| # Print the min max in fakequant | |
| for node in sess.graph.as_graph_def().node: | |
| if 'weights_quant/AssignMaxLast' in node.name \ | |
| or 'weights_quant/AssignMinLast' in node.name: | |
| tensor = sess.graph.get_tensor_by_name(node.name + ':0') | |
| print('{} = {}'.format(node.name, sess.run(tensor))) |
SandorSeres
commented
Jun 10, 2019
via email
Don't you need to set quantization during evaluation phase explicitly as it is set in the official README.md of quantization aware process: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize?
I think training graph can forward as well as backward.
Hence, we still can get each tensor from training graph.
The tensor of 'pred' output in training graph should be same as eval graph.
Hi,
It seems I done something wrong last time. :(
Now I used
docker run -it -v ${PWD}:/work tensorflow/tensorflow python /work/keras_quant.py
and it was running fine.
Now I will need to find it out how to put this model into Google Coral DevBoard TPU.
Have you tried it already?
Hi @rocking5566,
I am having trouble freezing the trained model. Did you manage to freeze the model for future inference purpose?
Hi,
I am trying to quantize a segmentation model. The model is all convolutional, yet I found out that only the last layer has fake quantization node. All the other convolutional layers are conv+bn+relu. The only layer with fake quantization node is just conv without bn or relu.
Did you manage to convert all the convolutional layers to fake quantization node?
Hi,
It seems I done something wrong last time. :(
Now I used
docker run -it -v ${PWD}:/work tensorflow/tensorflow python /work/keras_quant.py
and it was running fine.
Now I will need to find it out how to put this model into Google Coral DevBoard TPU.
Have you tried it already?
Hi @SandorSeres, did you succeed in implementing your model to Google Coral? I'm using TF instead of Keras, but also faced with quantization problems (BatchNorm specifically).