Cython LSTM.ipynb

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      "% load_ext autoreload\n",
      "% autoreload 2\n",
      "% matplotlib inline\n",
      "% load_ext cythonmagic\n",
      "% config InlineBackend.figure_format = 'svg'\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np, matplotlib\n",
      "from cython_lstm.network import Network\n",
      "from cython_lstm.neuron  import LogisticNeuron, TanhNeuron, SoftmaxNeuron\n",
      "from cython_lstm.layers  import LoopLayer, SliceLayer, ActivationLayer, LinearLayer\n",
      "from cython_lstm.trainer import Trainer\n",
      "from cython_lstm.error   import MSE, CategoricalCrossEntropy, BinaryCrossEntropy\n",
      "from cython_lstm.dataset import create_xor_dataset, create_digit_dataset\n",
      "import cython_lstm.network_viewer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
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     "input": [
      "def test_net():\n",
      "    # create a test dataset\n",
      "    xor_dataset, xor_labels = create_xor_dataset()\n",
      "    # create a small network:\n",
      "\n",
      "    net = Network(metric= BinaryCrossEntropy)\n",
      "    print(\"Initialization OK\")\n",
      "    first_layer       = LinearLayer(xor_dataset.shape[1], 6)\n",
      "    activation_layer  = ActivationLayer(LogisticNeuron)\n",
      "    first_layer.connect_to(activation_layer)\n",
      "\n",
      "    second_layer      = LinearLayer(6,  xor_labels.shape[1])\n",
      "    activation_layer.connect_to(second_layer)\n",
      "\n",
      "    second_activation_layer = ActivationLayer(LogisticNeuron)\n",
      "    second_layer.connect_to(second_activation_layer)\n",
      "\n",
      "    net.add_layer(first_layer, input=True)\n",
      "    net.add_layer(activation_layer)\n",
      "    net.add_layer(second_layer)\n",
      "    net.add_layer(activation_layer)\n",
      "    net.add_layer(second_layer)\n",
      "    net.add_layer(second_activation_layer, output=True)\n",
      "\n",
      "    print(\"Construction OK\")\n",
      "\n",
      "    net.clear()\n",
      "    print(\"Clearing OK\")\n",
      "    net.activate(xor_dataset)\n",
      "    print(\"Activation OK\")\n",
      "    net.backpropagate(xor_labels)\n",
      "    print(\"Backpropagation OK\")\n",
      "\n",
      "    for gparam, param in zip(net.get_gradients(), net.get_parameters()):\n",
      "        assert(gparam.shape == param.shape), \"Weight updates are not the same size\"\n",
      "\n",
      "    print(\"Updates and parameters shapes OK\")\n",
      "\n",
      "    trainer = Trainer(net, 0.3)\n",
      "    print(\"Trainer OK\")\n",
      "    epochs = 2000\n",
      "\n",
      "    for epoch in range(epochs):\n",
      "        er = trainer.train(xor_dataset, xor_labels)\n",
      "        if epoch > 0 and epoch % 250 == 0:\n",
      "            print(\"epoch %d, Error %.2f\" % (epoch, er))\n",
      "\n",
      "    print(\"Training OK\")\n",
      "\n",
      "    net.clear()\n",
      "    np.set_printoptions(precision=2)\n",
      "    passed_predictions = []\n",
      "    for data, prediction, label in zip(xor_dataset, net.activate(xor_dataset), xor_labels):\n",
      "        passed_predictions.append(np.allclose(prediction.round(), label))\n",
      "        print(\"%r => %r : %r\" % (data.astype(np.float64), np.around(prediction.astype(np.float64), decimals=2), passed_predictions[-1]))\n",
      "    if all(passed_predictions):\n",
      "        print(\"Learning OK\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_net()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initialization OK\n",
        "Construction OK\n",
        "Clearing OK\n",
        "Activation OK\n",
        "Backpropagation OK\n",
        "Updates and parameters shapes OK\n",
        "Trainer OK\n",
        "epoch 250, Error 5.53"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 500, Error 0.93"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 750, Error 0.39"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1000, Error 0.28"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1250, Error 0.22"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1500, Error 0.19"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1750, Error 0.17"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training OK"
       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "array([ 0.,  0.]) => array([ 0.02,  0.98]) : True\n",
        "array([ 0.,  1.]) => array([ 0.98,  0.02]) : True\n",
        "array([ 1.,  0.]) => array([ 0.98,  0.02]) : True\n",
        "array([ 1.,  1.]) => array([ 0.02,  0.98]) : True\n",
        "Learning OK\n"
       ]
      }
     ],
     "prompt_number": 3
    },
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     "cell_type": "code",
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     "input": [
      "def test_softmax_net():\n",
      "    # create a simple binary to decimal converter\n",
      "    digit_dataset, digit_labels = create_digit_dataset()\n",
      "    \n",
      "    # create a small network:\n",
      "\n",
      "    net = Network(metric = CategoricalCrossEntropy)\n",
      "    print(\"Initialization OK\")\n",
      "    first_layer = LinearLayer(digit_dataset.shape[1], 3)\n",
      "    first_layer_activation = ActivationLayer(LogisticNeuron)\n",
      "    first_layer.connect_to(first_layer_activation)\n",
      "    \n",
      "    second_layer = LinearLayer(3,  11) # 0, 1, ..., 9, 10\n",
      "    first_layer_activation.connect_to(second_layer)\n",
      "    \n",
      "    second_layer_activation = ActivationLayer(SoftmaxNeuron)\n",
      "    second_layer.connect_to(second_layer_activation)\n",
      "    \n",
      "    net.add_layer(first_layer, input=True)\n",
      "    net.add_layer(first_layer_activation)\n",
      "    net.add_layer(second_layer)\n",
      "    net.add_layer(second_layer_activation, output=True)\n",
      "    \n",
      "    print(\"Construction OK\")\n",
      "    net.clear()\n",
      "    print(\"Clearing OK\")\n",
      "    net.activate(digit_dataset)\n",
      "    print(\"Activation OK\")\n",
      "    net.backpropagate(digit_labels)\n",
      "    print(\"Backpropagation OK\")\n",
      "\n",
      "    for gparam, param in zip(net.get_gradients(), net.get_parameters()):\n",
      "        assert(gparam.shape == param.shape), \"Weight updates are not the same size\"\n",
      "        \n",
      "    print(\"Updates and parameters shapes OK\")\n",
      "    \n",
      "    trainer = Trainer(net, 0.01)\n",
      "    print(\"Trainer OK\")\n",
      "    epochs = 2000\n",
      "    \n",
      "    for epoch in range(epochs):\n",
      "        er = trainer.train(digit_dataset, digit_labels)\n",
      "        if epoch % 250 == 0:\n",
      "            print(\"epoch %d, Error %.2f\" % (epoch, er))\n",
      "        \n",
      "    print(\"Training OK\")\n",
      "    \n",
      "    net.clear()\n",
      "    np.set_printoptions(precision=2)\n",
      "    passed_predictions = []\n",
      "    plt.matshow(net.activate(digit_dataset), cmap = matplotlib.cm.Blues)\n",
      "    plt.xticks(np.arange(0,11), [str(w) for w in list(np.arange(0,11))])\n",
      "    plt.yticks(np.arange(0,11), [str(datum) for datum in digit_dataset])\n",
      "    plt.title(\"Prediction distribution for decimals from binary codes\")\n",
      "    \n",
      "    for data, prediction, label in zip(digit_dataset, net.activate(digit_dataset), digit_labels):\n",
      "        passed_predictions.append(prediction.argmax() ==  label)\n",
      "        print(\"%r => %r : %r\" % (data.astype(np.float64), prediction.argmax(), passed_predictions[-1]))\n",
      "    if all(passed_predictions):\n",
      "        print(\"Learning OK\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_softmax_net()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initialization OK\n",
        "Construction OK\n",
        "Clearing OK\n",
        "Activation OK\n",
        "Backpropagation OK\n",
        "Updates and parameters shapes OK\n",
        "Trainer OK\n",
        "epoch 0, Error 27.20\n",
        "epoch 250, Error 0.63"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 500, Error 0.28"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 750, Error 0.18"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1000, Error 0.13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1250, Error 0.10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1500, Error 0.08"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1750, Error 0.07"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training OK"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "array([ 0.,  0.,  0.,  0.]) => 0 : True"
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        "\n",
        "array([ 0.,  0.,  0.,  1.]) => 1 : True\n",
        "array([ 0.,  0.,  1.,  0.]) => 2 : True\n",
        "array([ 0.,  0.,  1.,  1.]) => 3 : True\n",
        "array([ 0.,  1.,  0.,  0.]) => 4 : True\n",
        "array([ 0.,  1.,  0.,  1.]) => 5 : True\n",
        "array([ 0.,  1.,  1.,  0.]) => 6 : True\n",
        "array([ 0.,  1.,  1.,  1.]) => 7 : True\n",
        "array([ 1.,  0.,  0.,  0.]) => 8 : True\n",
        "array([ 1.,  0.,  0.,  1.]) => 9 : True\n",
        "array([ 1.,  0.,  1.,  0.]) => 10 : True\n",
        "Learning OK\n"
       ]
      },
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      "def test_tensor_net():\n",
      "    # create a simple binary to decimal converter\n",
      "    digit_dataset, digit_labels = create_digit_dataset()\n",
      "    \n",
      "    # create a small network:\n",
      "\n",
      "    net = Network(metric=CategoricalCrossEntropy)\n",
      "    print(\"Initialization OK\")\n",
      "    first_layer = LinearLayer(digit_dataset.shape[1], 3)\n",
      "    first_layer_activation = ActivationLayer(LogisticNeuron)\n",
      "    first_layer.connect_to(first_layer_activation)\n",
      "    \n",
      "    second_layer = LinearLayer(3,  11, tensor=True) # 0, 1, ..., 9, 10\n",
      "    first_layer_activation.connect_to(second_layer)\n",
      "    \n",
      "    second_layer_activation = ActivationLayer(SoftmaxNeuron)\n",
      "    second_layer.connect_to(second_layer_activation)\n",
      "    \n",
      "    net.add_layer(first_layer, input=True)\n",
      "    net.add_layer(first_layer_activation)\n",
      "    net.add_layer(second_layer)\n",
      "    net.add_layer(second_layer_activation, output=True)\n",
      "    \n",
      "    print(\"Construction OK\")\n",
      "    \n",
      "    net.clear()\n",
      "    print(\"Clearing OK\")\n",
      "    net.activate(digit_dataset)\n",
      "    print(\"Activation OK\")\n",
      "    net.backpropagate(digit_labels)\n",
      "    print(\"Backpropagation OK\")\n",
      "\n",
      "    for gparam, param in zip(net.get_gradients(), net.get_parameters()):\n",
      "        assert(gparam.shape == param.shape), \"Weight updates are not the same size\"\n",
      "        \n",
      "    print(\"Updates and parameters shapes OK\")\n",
      "    \n",
      "    trainer = Trainer(net, 0.01)\n",
      "    print(\"Trainer OK\")\n",
      "    epochs = 2000\n",
      "    \n",
      "    for epoch in range(epochs):\n",
      "        er = trainer.train(digit_dataset, digit_labels)\n",
      "        if epoch % 250 == 0:\n",
      "            print(\"epoch %d, Error %.2f\" % (epoch, er))\n",
      "        \n",
      "    print(\"Training OK\")\n",
      "    \n",
      "    net.clear()\n",
      "    np.set_printoptions(precision=2)\n",
      "    passed_predictions = []\n",
      "    plt.matshow(net.activate(digit_dataset), cmap = matplotlib.cm.Blues)\n",
      "    plt.xticks(np.arange(0,11), [str(w) for w in list(np.arange(0,11))])\n",
      "    plt.yticks(np.arange(0,11), [str(datum) for datum in digit_dataset])\n",
      "    plt.title(\"Prediction distribution for decimals from binary codes\")\n",
      "    \n",
      "    for data, prediction, label in zip(digit_dataset, net.activate(digit_dataset), digit_labels):\n",
      "        passed_predictions.append(prediction.argmax() ==  label)\n",
      "        print(\"%r => %r : %r\" % (data.astype(np.float64), prediction.argmax(), passed_predictions[-1]))\n",
      "    if all(passed_predictions):\n",
      "        print(\"Learning OK\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
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     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initialization OK\n",
        "Construction OK\n",
        "Clearing OK\n",
        "Activation OK\n",
        "Backpropagation OK\n",
        "Updates and parameters shapes OK\n",
        "Trainer OK\n",
        "epoch 0, Error 27.47\n",
        "epoch 250, Error 0.13"
       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 500, Error 0.06"
       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 750, Error 0.04"
       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1000, Error 0.03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1250, Error 0.02"
       ]
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1500, Error 0.02"
       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 1750, Error 0.02"
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       "stream": "stdout",
       "text": [
        "\n",
        "Training OK"
       ]
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       "text": [
        "\n",
        "array([ 0.,  0.,  0.,  0.]) => 0 : True"
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        "array([ 0.,  0.,  0.,  1.]) => 1 : True\n",
        "array([ 0.,  0.,  1.,  0.]) => 2 : True\n",
        "array([ 0.,  0.,  1.,  1.]) => 3 : True\n",
        "array([ 0.,  1.,  0.,  0.]) => 4 : True\n",
        "array([ 0.,  1.,  0.,  1.]) => 5 : True\n",
        "array([ 0.,  1.,  1.,  0.]) => 6 : True\n",
        "array([ 0.,  1.,  1.,  1.]) => 7 : True\n",
        "array([ 1.,  0.,  0.,  0.]) => 8 : True\n",
        "array([ 1.,  0.,  0.,  1.]) => 9 : True\n",
        "array([ 1.,  0.,  1.,  0.]) => 10 : True\n",
        "Learning OK\n"
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      "def update_step(data, step):\n",
      "        data[step,:,:] = data[step-1,:,:]\n",
      "        data[step,:,0] += 1\n",
      "        for stream in range(data.shape[1]):\n",
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      "                data[step,stream,0] = 0\n",
      "                data[step,stream,1] += 1\n",
      "                if data[step,stream,1] > 1:\n",
      "                    data[step,stream,1] = 0\n",
      "                    data[step,stream,2] += 1\n",
      "                    if data[step,stream,2] > 1:\n",
      "                        data[step,stream,2] = 0\n",
      "            if data[step,stream,1] > 1:\n",
      "                data[step,stream,1] = 0\n",
      "                data[step,stream,2] += 1\n",
      "                if data[step,stream,2] > 1:\n",
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      "            if data[step,stream,2] > 1:\n",
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      "def binary_addition_data(TIMESTEPS = 20,\n",
      "                         DIFFERENT_OBSERVABLES = 3,\n",
      "                         OBSERVATION_DIMENSIONS = 3,\n",
      "                         NOISE_SIZE = 0.03):\n",
      "\n",
      "    recurrent_data = np.zeros([TIMESTEPS, DIFFERENT_OBSERVABLES, OBSERVATION_DIMENSIONS], dtype=np.float32)\n",
      "\n",
      "    start_step = np.random.randint(0, 1, size=(DIFFERENT_OBSERVABLES, OBSERVATION_DIMENSIONS))\n",
      "\n",
      "    def update_step(data, step):\n",
      "        data[step,:,:] = data[step-1,:,:]\n",
      "        data[step,:,0] += 1\n",
      "        for stream in range(data.shape[1]):\n",
      "            if data[step,stream,0] > 1:\n",
      "                data[step,stream,0] = 0\n",
      "                data[step,stream,1] += 1\n",
      "                if data[step,stream,1] > 1:\n",
      "                    data[step,stream,1] = 0\n",
      "                    data[step,stream,2] += 1\n",
      "                    if data[step,stream,2] > 1:\n",
      "                        data[step,stream,2] = 0\n",
      "            if data[step,stream,1] > 1:\n",
      "                data[step,stream,1] = 0\n",
      "                data[step,stream,2] += 1\n",
      "                if data[step,stream,2] > 1:\n",
      "                    data[step,stream,2] = 0\n",
      "            if data[step,stream,2] > 1:\n",
      "                data[step,stream,2] = 0\n",
      "    \n",
      "    recurrent_data[0,:,:] = start_step\n",
      "    for i in range(1, TIMESTEPS):\n",
      "        update_step(recurrent_data, i)\n",
      "    \n",
      "    noisy_data = recurrent_data + NOISE_SIZE * np.random.standard_normal(recurrent_data.shape).astype(np.float32)\n",
      "    return noisy_data, recurrent_data\n",
      "\n",
      "def one_trick_pony(network, temporal=False):\n",
      "    print(\"Simple binary additions using network:\")\n",
      "    for num in range(0, 6):\n",
      "        bin_repr = np.binary_repr(num)[::-1][:3]\n",
      "        if len(bin_repr) < 3:\n",
      "            bin_repr = bin_repr + (3 - len(bin_repr)) * \"0\"\n",
      "        if temporal:\n",
      "            bin_repr = np.array([[list(bin_repr)]])\n",
      "        else:\n",
      "            bin_repr = np.array([list(bin_repr)])\n",
      "        print(\"%d + 1 ~= %d\" % (num, sum(2 ** k if i > 0 else 0. for k, i in enumerate(network.activate(bin_repr)[0].round()))))\n",
      "\n",
      "\n",
      "def test_reccurent_net():\n",
      "    \n",
      "    # Binary addition problem\n",
      "\n",
      "    TIMESTEPS = 20\n",
      "    DIFFERENT_OBSERVABLES = 3\n",
      "    OBSERVATION_DIMENSIONS = 3\n",
      "    NOISE_SIZE = 0.03\n",
      "\n",
      "    noisy_data, recurrent_data = binary_addition_data(TIMESTEPS,\n",
      "                                                      DIFFERENT_OBSERVABLES,\n",
      "                                                      OBSERVATION_DIMENSIONS,\n",
      "                                                      NOISE_SIZE)\n",
      "    \n",
      "    HIDDEN_DIMENSIONS = 8\n",
      "\n",
      "    net = Network()\n",
      "    \n",
      "    input_layer = LinearLayer(OBSERVATION_DIMENSIONS, HIDDEN_DIMENSIONS)\n",
      "    activ_layer = ActivationLayer(TanhNeuron)\n",
      "    prediction_layer = LinearLayer(HIDDEN_DIMENSIONS, OBSERVATION_DIMENSIONS)\n",
      "    output_layer = ActivationLayer(LogisticNeuron)\n",
      "    \n",
      "    input_layer.connect_to(activ_layer)\n",
      "    activ_layer.connect_to(prediction_layer)\n",
      "    prediction_layer.connect_to(output_layer)\n",
      "    \n",
      "    temporal_loop = LoopLayer(OBSERVATION_DIMENSIONS, input_layer)\n",
      "    \n",
      "    slice_layer = SliceLayer((-1,-1))\n",
      "    temporal_loop.connect_to(slice_layer)\n",
      "\n",
      "    net.add_layer(temporal_loop, input=True)\n",
      "    net.add_layer(slice_layer, output=True)\n",
      "    net.set_error(BinaryCrossEntropy)\n",
      "    net.activate(noisy_data[:-1,:,:])\n",
      "    \n",
      "    net.backpropagate(recurrent_data[-1,:,:].astype(np.int32))\n",
      "    print(\"Backpropagation Through Time OK\")\n",
      "\n",
      "    for gparam, param in zip(net.get_gradients(), net.get_parameters()):\n",
      "        assert(gparam.shape == param.shape), \"Weight updates are not the same size\"\n",
      "        \n",
      "    print(\"Updates and parameters shapes OK\")\n",
      "    \n",
      "    trainer = Trainer(net, method=\"adadelta\", rho=0.95)\n",
      "    print(\"Trainer OK\")\n",
      "    epochs = 5000\n",
      "    subepochs = 10\n",
      "    \n",
      "    print(\"before we start, here's the network's view of addition:\")\n",
      "    \n",
      "    one_trick_pony(net, True)\n",
      "    \n",
      "    # use last time step for prediction\n",
      "    \n",
      "    # Note: if you look closely, this is a really poor\n",
      "    # example, since we are showing many useless observations\n",
      "    # and finally closing with one useful one for training\n",
      "    # and context is not useful in this instance for prediction.\n",
      "    \n",
      "    er = 0.\n",
      "    for epoch in range(epochs):\n",
      "        \n",
      "        for subepoch in range(subepochs):\n",
      "        \n",
      "            random_range_begin = np.random.randint(0, TIMESTEPS-5)\n",
      "            random_range_end = random_range_begin + 1#np.random.randint(random_range_begin+4, TIMESTEPS)\n",
      "\n",
      "            er += trainer.train(noisy_data[random_range_begin:random_range_end,:,:], recurrent_data[random_range_end,:,:].astype(np.int32))\n",
      "        \n",
      "        if epoch > 0 and epoch % 1000 == 0:\n",
      "            print(\"epoch %d, Error %.2f\" % (epoch * subepochs, er))\n",
      "            er = 0.\n",
      "\n",
      "    print(\"Training OK\")\n",
      "    \n",
      "    one_trick_pony(net, True)\n",
      "    \n",
      "    return net\n",
      "\n",
      "def test_binary_addition_net():\n",
      "    # Binary addition problem\n",
      "\n",
      "    TIMESTEPS = 200\n",
      "    DIFFERENT_OBSERVABLES = 10\n",
      "    OBSERVATION_DIMENSIONS = 3\n",
      "    NOISE_SIZE = 0.03\n",
      "\n",
      "    noisy_data, recurrent_data = binary_addition_data(TIMESTEPS,\n",
      "                                                      DIFFERENT_OBSERVABLES,\n",
      "                                                      OBSERVATION_DIMENSIONS,\n",
      "                                                      NOISE_SIZE)\n",
      "    \n",
      "    HIDDEN_DIMENSIONS = 8\n",
      "\n",
      "    net = Network()\n",
      "    \n",
      "    input_layer = LinearLayer(OBSERVATION_DIMENSIONS, HIDDEN_DIMENSIONS)\n",
      "    activ_layer = ActivationLayer(TanhNeuron)\n",
      "    prediction_layer = LinearLayer(HIDDEN_DIMENSIONS, OBSERVATION_DIMENSIONS)\n",
      "    output_layer = ActivationLayer(LogisticNeuron)\n",
      "    \n",
      "    input_layer.connect_to(activ_layer)\n",
      "    activ_layer.connect_to(prediction_layer)\n",
      "    prediction_layer.connect_to(output_layer)\n",
      "\n",
      "    net.add_layer(input_layer, input=True)\n",
      "    net.add_layer(activ_layer)\n",
      "    net.add_layer(prediction_layer)\n",
      "    net.add_layer(output_layer, output=True)\n",
      "    net.set_error(BinaryCrossEntropy)\n",
      "    net.activate(noisy_data[0,:,:])\n",
      "    \n",
      "    net.backpropagate(recurrent_data[1,:,:].astype(np.int32))\n",
      "\n",
      "    for gparam, param in zip(net.get_gradients(), net.get_parameters()):\n",
      "        assert(gparam.shape == param.shape), \"Weight updates are not the same size\"\n",
      "        \n",
      "    print(\"Updates and parameters shapes OK\")\n",
      "    \n",
      "    trainer = Trainer(net, method=\"adadelta\", rho=0.95)\n",
      "    print(\"Trainer OK\")\n",
      "    epochs = 5000\n",
      "    subepochs = 10\n",
      "    \n",
      "    print(\"before we start, here's the network's view of addition:\")\n",
      "    \n",
      "    one_trick_pony(net)\n",
      "    \n",
      "    er = 0.\n",
      "    for epoch in range(epochs):\n",
      "        \n",
      "        for subepoch in range(subepochs):\n",
      "        \n",
      "            random_range_begin = np.random.randint(0, TIMESTEPS-5)\n",
      "            random_range_end = random_range_begin +1\n",
      "\n",
      "            er += trainer.train(noisy_data[random_range_begin,:,:], recurrent_data[random_range_end,:,:].astype(np.int32))\n",
      "        \n",
      "        if epoch > 0 and epoch % 1000 == 0:\n",
      "            print(\"epoch %d, Error %.2f\" % (epoch * subepochs, er))\n",
      "            er = 0.\n",
      "\n",
      "    print(\"Training OK\")\n",
      "    \n",
      "    one_trick_pony(net)\n",
      "\n",
      "    return net"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 202
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flat_calculator_net = test_binary_addition_net()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Updates and parameters shapes OK\n",
        "Trainer OK\n",
        "before we start, here's the network's view of addition:\n",
        "Simple binary additions using network:\n",
        "0 + 1 ~= 6\n",
        "1 + 1 ~= 6\n",
        "2 + 1 ~= 6\n",
        "3 + 1 ~= 6\n",
        "4 + 1 ~= 6\n",
        "5 + 1 ~= 6\n",
        "epoch 10000, Error 35194.14"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 20000, Error 792.28"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 30000, Error 527.60"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 40000, Error 426.45"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training OK"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Simple binary additions using network:\n",
        "0 + 1 ~= 1\n",
        "1 + 1 ~= 2\n",
        "2 + 1 ~= 3\n",
        "3 + 1 ~= 4\n",
        "4 + 1 ~= 5\n",
        "5 + 1 ~= 6\n"
       ]
      }
     ],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recurrent_net = test_reccurent_net()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Backpropagation Through Time OK\n",
        "Updates and parameters shapes OK\n",
        "Trainer OK\n",
        "before we start, here's the network's view of addition:\n",
        "Simple binary additions using network:\n",
        "0 + 1 ~= 0\n",
        "1 + 1 ~= 0\n",
        "2 + 1 ~= 0\n",
        "3 + 1 ~= 0\n",
        "4 + 1 ~= 0\n",
        "5 + 1 ~= 0\n",
        "epoch 10000, Error 12438.66"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 20000, Error 701.64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 30000, Error 329.03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 40000, Error 251.96"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training OK"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Simple binary additions using network:\n",
        "0 + 1 ~= 1\n",
        "1 + 1 ~= 2\n",
        "2 + 1 ~= 3\n",
        "3 + 1 ~= 4\n",
        "4 + 1 ~= 5\n",
        "5 + 1 ~= 6\n"
       ]
      }
     ],
     "prompt_number": 200
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Drawing the network"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net = Network()\n",
      "first_layer = Layer(3, neuron=TanhNeuron)\n",
      "net.add_layer(first_layer, input=True)\n",
      "second_layer = Layer(5, 2, neuron=SoftmaxNeuron)\n",
      "net.add_layer(second_layer, output=True)\n",
      "first_layer.connect_to(second_layer)\n",
      "cython_lstm.network_viewer.draw(net)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Future APIs issues and ideas:\n",
      "\n",
      "* Prevent parameter duplication by adding uuids to each, and using sets to identify them (at least initially)\n",
      "\n",
      "* Slice Layer, and add layer are clunky ways of achieving simple things. These should implicity act on the layer and connect it.\n",
      "\n",
      "* In doing so the network should also implicitly gobble up all the resulting layers into a coherent whole.\n",
      "\n",
      "* Optimization is then a matter of preventing the saving of the non transformed input of a linear layer, and converting all these classes either to Numba or Cython for compilation to avoid doing all sorts of polymorphic checks everywhere.\n",
      "\n",
      "## Future Steps:\n",
      "\n",
      "* Implement RNN with memory\n",
      "\n",
      "* Implement gate unit by overloading the `__add__` operator\n",
      "\n",
      "* Implement the loop layer in Cython with nogil where possible\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def topology_test():\n",
      "    # create a test dataset\n",
      "    xor_dataset, xor_labels = create_xor_dataset()\n",
      "    # create a small network:\n",
      "\n",
      "    net = Network(metric = BinaryCrossEntropy)\n",
      "    print(\"Initialization OK\")\n",
      "    first_layer       = LinearLayer(xor_dataset.shape[1], 6)\n",
      "    activation_layer  = ActivationLayer(LogisticNeuron)\n",
      "    \n",
      "    first_layer.connect_to(activation_layer)\n",
      "    \n",
      "    second_input = cython_lstm.network.DataLayer()\n",
      "    \n",
      "    second_layer = LinearLayer(3, 6)\n",
      "    second_input.connect_to(second_layer)\n",
      "    \n",
      "    third_layer = activation_layer + second_layer\n",
      "    \n",
      "    net.add_layer(first_layer, input=True)\n",
      "    net.add_layer(activation_layer)\n",
      "    net.add_layer(second_layer)\n",
      "    net.add_layer(second_input)\n",
      "    net.add_layer(third_layer, output=True)\n",
      "    return net\n",
      "    \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = topology_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Initialization OK\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[b.layer for b in a.topsort()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "[<cython_lstm.network.DataLayer at 0x110fbed30>,\n",
        " <LinearLayer {'output_size': 6, 'activation': '', 'input_size': 3}>,\n",
        " <cython_lstm.network.DataLayer at 0x110f78a20>,\n",
        " <LinearLayer {'output_size': 6, 'activation': '', 'input_size': 2}>,\n",
        " <ActivationLayer {'output_size': '', 'activation': 'Sigmoid', 'input_size': ''}>,\n",
        " <ElementWiseSum {'output_size': '', 'activation': '', 'input_size': ''}>]"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
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     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
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