model getter fixed
This commit is contained in:
Si11ium
382
code/methods.py
382
code/methods.py
@ -1,191 +1,191 @@
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import tensorflow as tf
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from keras.models import Sequential, Model
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from keras.layers import SimpleRNN, Dense
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from keras.layers import Input, TimeDistributed
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from tqdm import tqdm
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import time
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import os
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import dill
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from experiment import Experiment
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import itertools
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from typing import Union
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import numpy as np
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class Network(object):
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def __init__(self, features, cells, layers, bias=False, recurrent=False):
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self.features = features
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self.cells = cells
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self.num_layer = layers
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bias_params = cells if bias else 0
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# Recurrent network
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if recurrent:
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# First RNN
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p_layer_1 = (self.features * self.cells + self.cells ** 2 + bias_params)
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# All other RNN Layers
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p_layer_n = (self.cells * self.cells + self.cells ** 2 + bias_params) * (self.num_layer - 1)
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else:
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# First Dense
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p_layer_1 = (self.features * self.cells + bias_params)
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# All other Dense Layers
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p_layer_n = (self.cells * self.cells + bias_params) * (self.num_layer - 1)
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# Final Dense
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p_layer_out = self.features * self.cells + bias_params
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self.parameters = np.sum([p_layer_1, p_layer_n, p_layer_out])
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# Build network
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cell = SimpleRNN if recurrent else Dense
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self.inputs, x = Input(shape=(self.parameters // self.features,
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self.features) if recurrent else (self.features,)), None
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for layer in range(self.num_layer):
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if recurrent:
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x = SimpleRNN(self.cells, activation=None, use_bias=False,
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return_sequences=True)(self.inputs if layer == 0 else x)
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else:
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x = Dense(self.cells, activation=None, use_bias=False,
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)(self.inputs if layer == 0 else x)
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self.outputs = Dense(self.features if recurrent else 1, activation=None, use_bias=False)(x)
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print('Network initialized, i haz {p} params @:{e}Features: {f}{e}Cells: {c}{e}Layers: {l}'.format(
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p=self.parameters, l=self.num_layer, c=self.cells, f=self.features, e='\n{}'.format(' ' * 5))
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)
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pass
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def get_inputs(self):
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return self.inputs
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def get_outputs(self):
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return self.outputs
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class _BaseNetwork(Model):
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def __init__(self, **kwargs):
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super(_BaseNetwork, self).__init__(**kwargs)
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# This is dirty
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self.features = None
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def get_weights_flat(self):
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weights = super().get_weights()
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flat = np.asarray(np.concatenate([x.flatten() for x in weights]))
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return flat
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def step(self, x):
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pass
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def step_other(self, other: Union[Sequential, Model]) -> bool:
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pass
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def get_parameter_count(self):
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return np.sum([np.prod(x.shape) for x in self.get_weights()])
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def train_on_batch(self, *args, **kwargs):
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raise NotImplementedError
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def compile(self, *args, **kwargs):
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raise NotImplementedError
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@staticmethod
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def mean_abs_error(labels, predictions):
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return np.mean(np.abs(predictions - labels), axis=-1)
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@staticmethod
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def mean_sqrd_error(labels, predictions):
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return np.mean(np.square(predictions - labels), axis=-1)
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class RecurrentNetwork(_BaseNetwork):
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def __init__(self, network: Network, *args, **kwargs):
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super().__init__(inputs=network.inputs, outputs=network.outputs)
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self.features = network.features
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self.parameters = network.parameters
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assert self.parameters == self.get_parameter_count()
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def step(self, x):
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shaped = np.reshape(x, (1, -1, self.features))
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return self.predict(shaped).flatten()
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def fit(self, epochs=500, **kwargs):
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losses = []
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with tqdm(total=epochs, ascii=True,
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desc='Type: {t}'. format(t=self.__class__.__name__),
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postfix=["Loss", dict(value=0)]) as bar:
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for _ in range(epochs):
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x = self.get_weights_flat()
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y = self.step(x)
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weights = self.get_weights()
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global_idx = 0
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for idx, weight_matrix in enumerate(weights):
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flattened = weight_matrix.flatten()
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new_weights = y[global_idx:global_idx + flattened.shape[0]]
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weights[idx] = np.reshape(new_weights, weight_matrix.shape)
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global_idx += flattened.shape[0]
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losses.append(self.mean_sqrd_error(y.flatten(), self.get_weights_flat()))
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self.set_weights(weights)
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bar.postfix[1]["value"] = losses[-1]
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bar.update()
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return losses
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class FeedForwardNetwork(_BaseNetwork):
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def __init__(self, network:Network, **kwargs):
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super().__init__(inputs=network.inputs, outputs=network.outputs, **kwargs)
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self.features = network.features
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self.parameters = network.parameters
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self.num_layer = network.num_layer
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self.num_cells = network.cells
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# assert self.parameters == self.get_parameter_count()
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def step(self, x):
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return self.predict(x)
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def step_other(self, x):
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return self.predict(x)
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def fit(self, epochs=500, **kwargs):
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losses = []
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with tqdm(total=epochs, ascii=True,
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desc='Type: {t} @ Epoch:'. format(t=self.__class__.__name__),
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postfix=["Loss", dict(value=0)]) as bar:
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for _ in range(epochs):
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all_weights = self.get_weights_flat()
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cell_idx = np.apply_along_axis(lambda x: x/self.num_cells, 0, np.arange(int(self.get_parameter_count())))
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xc = np.concatenate((all_weights[..., None], cell_idx[..., None]), axis=1)
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y = self.step(xc)
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weights = self.get_weights()
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global_idx = 0
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for idx, weight_matrix in enumerate(weights):
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# UPDATE THE WEIGHTS
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flattened = weight_matrix.flatten()
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new_weights = y[global_idx:global_idx + flattened.shape[0], 0]
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weights[idx] = np.reshape(new_weights, weight_matrix.shape)
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global_idx += flattened.shape[0]
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losses.append(self.mean_sqrd_error(y[:, 0].flatten(), self.get_weights_flat()))
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self.set_weights(weights)
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bar.postfix[1]["value"] = losses[-1]
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bar.update()
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return losses
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if __name__ == '__main__':
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with Experiment() as exp:
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features, cells, layers = 2, 2, 2
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use_recurrent = False
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if use_recurrent:
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network = Network(features, cells, layers, recurrent=use_recurrent)
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r = RecurrentNetwork(network)
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loss = r.fit(epochs=10)
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exp.save(rnet=r)
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else:
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network = Network(features, cells, layers, recurrent=use_recurrent)
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ff = FeedForwardNetwork(network)
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loss = ff.fit(epochs=10)
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exp.save(ffnet=ff)
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print(loss)
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import tensorflow as tf
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from keras.models import Sequential, Model
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from keras.layers import SimpleRNN, Dense
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from keras.layers import Input, TimeDistributed
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from tqdm import tqdm
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import time
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import os
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import dill
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from experiment import Experiment
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import itertools
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from typing import Union
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import numpy as np
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class Network(object):
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def __init__(self, features, cells, layers, bias=False, recurrent=False):
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self.features = features
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self.cells = cells
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self.num_layer = layers
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bias_params = cells if bias else 0
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# Recurrent network
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if recurrent:
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# First RNN
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p_layer_1 = (self.features * self.cells + self.cells ** 2 + bias_params)
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# All other RNN Layers
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p_layer_n = (self.cells * self.cells + self.cells ** 2 + bias_params) * (self.num_layer - 1)
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else:
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# First Dense
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p_layer_1 = (self.features * self.cells + bias_params)
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# All other Dense Layers
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p_layer_n = (self.cells * self.cells + bias_params) * (self.num_layer - 1)
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# Final Dense
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p_layer_out = self.features * self.cells + bias_params
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self.parameters = np.sum([p_layer_1, p_layer_n, p_layer_out])
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# Build network
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cell = SimpleRNN if recurrent else Dense
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self.inputs, x = Input(shape=(self.parameters // self.features,
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self.features) if recurrent else (self.features,)), None
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for layer in range(self.num_layer):
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if recurrent:
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x = SimpleRNN(self.cells, activation=None, use_bias=False,
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return_sequences=True)(self.inputs if layer == 0 else x)
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else:
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x = Dense(self.cells, activation=None, use_bias=False,
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)(self.inputs if layer == 0 else x)
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self.outputs = Dense(self.features if recurrent else 1, activation=None, use_bias=False)(x)
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print('Network initialized, i haz {p} params @:{e}Features: {f}{e}Cells: {c}{e}Layers: {l}'.format(
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p=self.parameters, l=self.num_layer, c=self.cells, f=self.features, e='\n{}'.format(' ' * 5))
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)
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pass
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def get_inputs(self):
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return self.inputs
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def get_outputs(self):
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return self.outputs
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class _BaseNetwork(Model):
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def __init__(self, **kwargs):
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super(_BaseNetwork, self).__init__(**kwargs)
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# This is dirty
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self.features = None
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def get_weights_flat(self):
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weights = super().get_weights()
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flat = np.asarray(np.concatenate([x.flatten() for x in weights]))
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return flat
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def step(self, x):
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pass
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def step_other(self, other: Union[Sequential, Model]) -> bool:
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pass
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def get_parameter_count(self):
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return np.sum([np.prod(x.shape) for x in self.get_weights()])
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def train_on_batch(self, *args, **kwargs):
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raise NotImplementedError
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def compile(self, *args, **kwargs):
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raise NotImplementedError
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@staticmethod
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def mean_abs_error(labels, predictions):
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return np.mean(np.abs(predictions - labels), axis=-1)
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@staticmethod
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def mean_sqrd_error(labels, predictions):
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return np.mean(np.square(predictions - labels), axis=-1)
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class RecurrentNetwork(_BaseNetwork):
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def __init__(self, network: Network, *args, **kwargs):
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super().__init__(inputs=network.inputs, outputs=network.outputs)
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self.features = network.features
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self.parameters = network.parameters
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assert self.parameters == self.get_parameter_count()
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def step(self, x):
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shaped = np.reshape(x, (1, -1, self.features))
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return self.predict(shaped).flatten()
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def fit(self, epochs=500, **kwargs):
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losses = []
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with tqdm(total=epochs, ascii=True,
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desc='Type: {t}'. format(t=self.__class__.__name__),
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postfix=["Loss", dict(value=0)]) as bar:
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for _ in range(epochs):
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x = self.get_weights_flat()
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y = self.step(x)
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weights = self.get_weights()
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global_idx = 0
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for idx, weight_matrix in enumerate(weights):
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flattened = weight_matrix.flatten()
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new_weights = y[global_idx:global_idx + flattened.shape[0]]
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weights[idx] = np.reshape(new_weights, weight_matrix.shape)
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global_idx += flattened.shape[0]
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losses.append(self.mean_sqrd_error(y.flatten(), self.get_weights_flat()))
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self.set_weights(weights)
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bar.postfix[1]["value"] = losses[-1]
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bar.update()
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return losses
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class FeedForwardNetwork(_BaseNetwork):
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def __init__(self, network:Network, **kwargs):
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super().__init__(inputs=network.inputs, outputs=network.outputs, **kwargs)
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self.features = network.features
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self.parameters = network.parameters
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self.num_layer = network.num_layer
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self.num_cells = network.cells
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# assert self.parameters == self.get_parameter_count()
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def step(self, x):
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return self.predict(x)
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def step_other(self, x):
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return self.predict(x)
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def fit(self, epochs=500, **kwargs):
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losses = []
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with tqdm(total=epochs, ascii=True,
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desc='Type: {t} @ Epoch:'. format(t=self.__class__.__name__),
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postfix=["Loss", dict(value=0)]) as bar:
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for _ in range(epochs):
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all_weights = self.get_weights_flat()
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cell_idx = np.apply_along_axis(lambda x: x/self.num_cells, 0, np.arange(int(self.get_parameter_count())))
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xc = np.concatenate((all_weights[..., None], cell_idx[..., None]), axis=1)
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y = self.step(xc)
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weights = self.get_weights()
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global_idx = 0
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for idx, weight_matrix in enumerate(weights):
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# UPDATE THE WEIGHTS
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flattened = weight_matrix.flatten()
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new_weights = y[global_idx:global_idx + flattened.shape[0], 0]
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weights[idx] = np.reshape(new_weights, weight_matrix.shape)
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global_idx += flattened.shape[0]
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losses.append(self.mean_sqrd_error(y[:, 0].flatten(), self.get_weights_flat()))
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self.set_weights(weights)
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bar.postfix[1]["value"] = losses[-1]
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bar.update()
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return losses
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if __name__ == '__main__':
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with Experiment() as exp:
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features, cells, layers = 2, 2, 2
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use_recurrent = False
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if use_recurrent:
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network = Network(features, cells, layers, recurrent=use_recurrent)
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r = RecurrentNetwork(network)
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loss = r.fit(epochs=10)
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exp.save(rnet=r)
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else:
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network = Network(features, cells, layers, recurrent=use_recurrent)
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ff = FeedForwardNetwork(network)
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loss = ff.fit(epochs=10)
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exp.save(ffnet=ff)
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print(loss)
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