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https://github.com/data61/MP-SPDZ.git
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101 lines
2.8 KiB
Plaintext
101 lines
2.8 KiB
Plaintext
from Compiler.ml import keras
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import Compiler.ml as tf
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try:
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n_epochs = int(program.args[1])
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except (ValueError, IndexError):
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n_epochs = 20
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try:
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batch_size = int(program.args[2])
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except (ValueError, IndexError):
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batch_size = 128
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try:
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n_threads = int(program.args[3])
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except (ValueError, IndexError):
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n_threads = 36
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#Instantiation
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AlexNet = []
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padding = 'same'
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batchnorm = 'batchnorm' in program.args
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#1st Convolutional Layer
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AlexNet.append(keras.layers.Conv2D(filters=96, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding=9))
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AlexNet.append(keras.layers.Activation('relu'))
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AlexNet.append(keras.layers.MaxPooling2D(pool_size=3, strides=(2,2)))
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if batchnorm:
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AlexNet.append(keras.layers.BatchNormalization())
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#2nd Convolutional Layer
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AlexNet.append(keras.layers.Conv2D(filters=256, kernel_size=(5, 5), strides=(1,1), padding=1))
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AlexNet.append(keras.layers.Activation('relu'))
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if batchnorm:
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AlexNet.append(keras.layers.BatchNormalization())
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AlexNet.append(keras.layers.MaxPooling2D(pool_size=(2,2), strides=1))
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#3rd Convolutional Layer
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AlexNet.append(keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding=1))
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AlexNet.append(keras.layers.Activation('relu'))
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#4th Convolutional Layer
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AlexNet.append(keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding=1))
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AlexNet.append(keras.layers.Activation('relu'))
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#5th Convolutional Layer
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AlexNet.append(keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding=1))
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AlexNet.append(keras.layers.Activation('relu'))
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#Passing it to a Fully Connected layer
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# 1st Fully Connected Layer
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AlexNet.append(keras.layers.Dense(256))
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AlexNet.append(keras.layers.Activation('relu'))
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if 'dropout' in program.args:
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AlexNet.append(keras.layers.Dropout(0.5))
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#2nd Fully Connected Layer
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AlexNet.append(keras.layers.Dense(256))
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AlexNet.append(keras.layers.Activation('relu'))
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if 'dropout' in program.args:
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AlexNet.append(keras.layers.Dropout(0.5))
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#Output Layer
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AlexNet.append(keras.layers.Dense(10))
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tf.set_n_threads(n_threads)
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program.options_from_args()
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sfix.set_precision_from_args(program, adapt_ring=True)
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training_samples = MultiArray([50000, 32, 32, 3], sfix)
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training_labels = MultiArray([50000, 10], sint)
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test_samples = MultiArray([10000, 32, 32, 3], sfix)
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test_labels = MultiArray([10000, 10], sint)
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if 'no_acc' not in program.args:
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training_labels.input_from(0)
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training_samples.input_from(0)
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test_labels.input_from(0)
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test_samples.input_from(0)
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model = tf.keras.models.Sequential(AlexNet)
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model.compile_by_args(program)
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model.build(training_samples.sizes)
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model.summary()
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opt = model.fit(
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training_samples,
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training_labels,
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epochs=n_epochs,
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batch_size=batch_size,
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validation_data=(test_samples, test_labels)
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)
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