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import numpy as np | |
from keras.layers import Dense, Dropout, Activation, Flatten | |
from keras.layers import Conv1D, Conv2D, MaxPooling2D, MaxPooling1D, AveragePooling2D, GRU | |
from keras.layers import TimeDistributed, Bidirectional | |
from keras.layers.recurrent import LSTM | |
from keras.models import Sequential | |
def sigmoid(z): | |
s = 1.0 / (1.0 + np.exp(-1.0 * z)) | |
return s |
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// Copyright 2016, Tobias Hermann. | |
// https://github.com/Dobiasd/frugally-deep | |
// Distributed under the MIT License. | |
// (See accompanying LICENSE file or at | |
// https://opensource.org/licenses/MIT) | |
#define DOCTEST_CONFIG_IMPLEMENT_WITH_MAIN | |
#include "doctest.h" | |
#include <Eigen/Core> |
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#pragma once | |
#include "fdeep/layers/layer.hpp" | |
#include <string> | |
#include <functional> | |
namespace fdeep | |
{ | |
namespace internal |