148 lines
4.4 KiB
Python
148 lines
4.4 KiB
Python
from keras_core import operations as ops
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from keras_core import backend
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from keras_core.optimizers import SGD
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from keras_core.layers.layer import Layer
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from keras_core import initializers
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import jax
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from jax import numpy as jnp
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import numpy as np
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class MiniDense(Layer):
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def __init__(self, units, name=None):
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super().__init__(name=name)
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self.units = units
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def build(self, input_shape):
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input_dim = input_shape[-1]
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w_shape = (input_dim, self.units)
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w_value = initializers.GlorotUniform()(w_shape)
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self.w = backend.Variable(w_value)
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b_shape = (self.units,)
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b_value = initializers.Zeros()(b_shape)
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self.b = backend.Variable(b_value)
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def call(self, inputs):
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return ops.matmul(inputs, self.w) + self.b
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class MiniDropout(Layer):
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def __init__(self, rate, name=None):
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super().__init__(name=name)
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self.rate = rate
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self.seed_generator = backend.random.RandomSeedGenerator(1337)
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def call(self, inputs):
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return backend.random.dropout(inputs, self.rate, seed=self.seed_generator)
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class MiniBatchNorm(Layer):
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def __init__(self, name=None):
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super().__init__(name=name)
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self.epsilon = 1e-5
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self.momentum = 0.99
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def build(self, input_shape):
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shape = (input_shape[-1],)
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self.mean = backend.Variable(initializers.Zeros()(shape), trainable=False)
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self.variance = backend.Variable(
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initializers.GlorotUniform()(shape), trainable=False
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)
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self.beta = backend.Variable(initializers.Zeros()(shape))
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self.gamma = backend.Variable(initializers.Ones()(shape))
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def call(self, inputs, training=False):
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if training:
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mean = jnp.mean(inputs, axis=(0,)) # TODO: extend to rank 3+
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variance = jnp.var(inputs, axis=(0,))
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outputs = (inputs - mean) / (variance + self.epsilon)
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self.variance.assign(
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self.variance * self.momentum + variance * (1.0 - self.momentum)
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)
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self.mean.assign(self.mean * self.momentum + mean * (1.0 - self.momentum))
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else:
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outputs = (inputs - self.mean) / (self.variance + self.epsilon)
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outputs *= self.gamma
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outputs += self.beta
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return outputs
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class MyModel(Layer):
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def __init__(self, units, num_classes):
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super().__init__()
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self.dense1 = MiniDense(units)
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self.bn = MiniBatchNorm()
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self.dropout = MiniDropout(0.5)
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self.dense2 = MiniDense(num_classes)
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def call(self, x):
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x = self.dense1(x)
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x = self.bn(x)
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x = self.dropout(x)
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return self.dense2(x)
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def Dataset():
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for _ in range(10):
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yield (np.random.random((8, 4)), np.random.random((8, 2)))
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def loss_fn(y_true, y_pred):
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return ops.sum((y_true - y_pred) ** 2)
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optimizer = SGD()
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model = MyModel(8, 2)
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dataset = Dataset()
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# Build model
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x = ops.convert_to_tensor(np.random.random((8, 4)))
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model(x)
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# Build optimizer
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optimizer.build(model.trainable_variables)
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################################
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## Currently operational workflow
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def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y):
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y_pred, non_trainable_variables = model.stateless_call(
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trainable_variables, non_trainable_variables, x
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)
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loss = loss_fn(y, y_pred)
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return loss, non_trainable_variables
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grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)
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@jax.jit
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def train_step(trainable_variables, non_trainable_variables, optimizer_variables, x, y):
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(loss, non_trainable_variables), grads = grad_fn(
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trainable_variables, non_trainable_variables, x, y
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)
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trainable_variables, optimizer_variables = optimizer.stateless_apply(
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grads, optimizer_variables
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)
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return trainable_variables, non_trainable_variables, optimizer_variables
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# Training loop
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trainable_variables = model.trainable_variables
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non_trainable_variables = model.non_trainable_variables
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optimizer_variables = optimizer.variables
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for x, y in dataset:
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trainable_variables, non_trainable_variables, optimizer_variables = train_step(
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trainable_variables, non_trainable_variables, optimizer_variables, x, y
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)
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# Post-processing model state update
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for variable, value in zip(model.trainable_variables, trainable_variables):
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variable.assign(value)
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for variable, value in zip(model.non_trainable_variables, non_trainable_variables):
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variable.assign(value)
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print("Updated values")
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