STN_Train.ipynb

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     "text": [
      "Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled, cuDNN 5110)\n",
      "/usr/local/lib/python2.7/dist-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
      "  warnings.warn(warn)\n",
      "/usr/local/lib/python2.7/dist-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.\n",
      "  \"downsample module has been moved to the theano.tensor.signal.pool module.\")\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "os.environ['THEANO_FLAGS']='device=gpu0'\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "import lasagne\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "conv = lasagne.layers.Conv2DLayer\n",
    "pool = lasagne.layers.MaxPool2DLayer\n",
    "\n",
    "from lasagne.utils import as_tuple, floatX\n",
    "from lasagne.random import get_rng\n",
    "import cv2\n",
    "\n",
    "NUM_EPOCHS = 101\n",
    "BATCH_SIZE = 256\n",
    "LEARNING_RATE = 0.001\n",
    "DIM = 60\n",
    "NUM_CLASSES = 10\n",
    "mnist_cluttered = \"mnist_cluttered_60x60_6distortions.npz\"\n",
    "#INPUT FILE DEGISTIRILECEK"
   ]
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    "class TransformerLayer(lasagne.layers.MergeLayer):\n",
    "    \"\"\"\n",
    "    Spatial transformer layer\n",
    "    The layer applies an affine transformation on the input. The affine\n",
    "    transformation is parameterized with six learned parameters [1]_.\n",
    "    The output is interpolated with a bilinear transformation.\n",
    "    Parameters\n",
    "    ----------\n",
    "    incoming : a :class:`Layer` instance or a tuple\n",
    "        The layer feeding into this layer, or the expected input shape. The\n",
    "        output of this layer should be a 4D tensor, with shape\n",
    "        ``(batch_size, num_input_channels, input_rows, input_columns)``.\n",
    "    localization_network : a :class:`Layer` instance\n",
    "        The network that calculates the parameters of the affine\n",
    "        transformation. See the example for how to initialize to the identity\n",
    "        transform.\n",
    "    downsample_factor : float or iterable of float\n",
    "        A float or a 2-element tuple specifying the downsample factor for the\n",
    "        output image (in both spatial dimensions). A value of 1 will keep the\n",
    "        original size of the input. Values larger than 1 will downsample the\n",
    "        input. Values below 1 will upsample the input.\n",
    "    References\n",
    "    ----------\n",
    "    .. [1]  Max Jaderberg, Karen Simonyan, Andrew Zisserman,\n",
    "            Koray Kavukcuoglu (2015):\n",
    "            Spatial Transformer Networks. NIPS 2015,\n",
    "            http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf\n",
    "    Examples\n",
    "    --------\n",
    "    Here we set up the layer to initially do the identity transform, similarly\n",
    "    to [1]_. Note that you will want to use a localization with linear output.\n",
    "    If the output from the localization networks is [t1, t2, t3, t4, t5, t6]\n",
    "    then t1 and t5 determines zoom, t2 and t4 determines skewness, and t3 and\n",
    "    t6 move the center position.\n",
    "    >>> import numpy as np\n",
    "    >>> import lasagne\n",
    "    >>> b = np.zeros((2, 3), dtype='float32')\n",
    "    >>> b[0, 0] = 1\n",
    "    >>> b[1, 1] = 1\n",
    "    >>> b = b.flatten()  # identity transform\n",
    "    >>> W = lasagne.init.Constant(0.0)\n",
    "    >>> l_in = lasagne.layers.InputLayer((None, 3, 28, 28))\n",
    "    >>> l_loc = lasagne.layers.DenseLayer(l_in, num_units=6, W=W, b=b,\n",
    "    ... nonlinearity=None)\n",
    "    >>> l_trans = lasagne.layers.TransformerLayer(l_in, l_loc)\n",
    "    \"\"\"\n",
    "    def __init__(self, incoming, localization_network, downsample_factor=1,\n",
    "                 **kwargs):\n",
    "        super(TransformerLayer, self).__init__(\n",
    "            [incoming, localization_network], **kwargs)\n",
    "        self.downsample_factor = as_tuple(downsample_factor, 2)\n",
    "\n",
    "        #self.y has been added.\n",
    "        input_shp, loc_shp = self.input_shapes\n",
    "\n",
    "        if loc_shp[-1] != 6 or len(loc_shp) != 2:\n",
    "            raise ValueError(\"The localization network must have \"\n",
    "                             \"output shape: (batch_size, 6)\")\n",
    "        if len(input_shp) != 4:\n",
    "            raise ValueError(\"The input network must have a 4-dimensional \"\n",
    "                             \"output shape: (batch_size, num_input_channels, \"\n",
    "                             \"input_rows, input_columns)\")\n",
    "\n",
    "    def get_output_shape_for(self, input_shapes):\n",
    "        shape = input_shapes[0]\n",
    "        factors = self.downsample_factor\n",
    "        return (shape[:2] + tuple(None if s is None else int(s // f)\n",
    "                                  for s, f in zip(shape[2:], factors)))\n",
    "\n",
    "    def get_output_for(self, inputs, **kwargs):\n",
    "        # see eq. (1) and sec 3.1 in [1]\n",
    "        input, theta = inputs\n",
    "        self.x, self.y = _transform_affine(theta, input, self.downsample_factor)\n",
    "        return self.x\n",
    "    \n",
    "def _meshgrid(height, width):\n",
    "    # This function is the grid generator from eq. (1) in reference [1].\n",
    "    # It is equivalent to the following numpy code:\n",
    "    #  x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),\n",
    "    #                         np.linspace(-1, 1, height))\n",
    "    #  ones = np.ones(np.prod(x_t.shape))\n",
    "    #  grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])\n",
    "    # It is implemented in Theano instead to support symbolic grid sizes.\n",
    "    # Note: If the image size is known at layer construction time, we could\n",
    "    # compute the meshgrid offline in numpy instead of doing it dynamically\n",
    "    # in Theano. However, it hardly affected performance when we tried.\n",
    "    x_t = T.dot(T.ones((height, 1)),\n",
    "                _linspace(-1.0, 1.0, width).dimshuffle('x', 0))\n",
    "    y_t = T.dot(_linspace(-1.0, 1.0, height).dimshuffle(0, 'x'),\n",
    "                T.ones((1, width)))\n",
    "\n",
    "    x_t_flat = x_t.reshape((1, -1))\n",
    "    y_t_flat = y_t.reshape((1, -1))\n",
    "    ones = T.ones_like(x_t_flat)\n",
    "    grid = T.concatenate([x_t_flat, y_t_flat, ones], axis=0)\n",
    "    return grid\n",
    "    \n",
    "def _transform_affine(theta, input, downsample_factor):\n",
    "    num_batch, num_channels, height, width = input.shape\n",
    "    theta = T.reshape(theta, (-1, 2, 3))\n",
    "\n",
    "    # grid of (x_t, y_t, 1), eq (1) in ref [1]\n",
    "    out_height = T.cast(height // downsample_factor[0], 'int64')\n",
    "    out_width = T.cast(width // downsample_factor[1], 'int64')\n",
    "    grid = _meshgrid(out_height, out_width)\n",
    "\n",
    "    # Transform A x (x_t, y_t, 1)^T -> (x_s, y_s)\n",
    "    T_g = T.dot(theta, grid)\n",
    "    x_s = T_g[:, 0]\n",
    "    y_s = T_g[:, 1]\n",
    "    x_s_flat = x_s.flatten()\n",
    "    y_s_flat = y_s.flatten()\n",
    "\n",
    "    # dimshuffle input to  (bs, height, width, channels)\n",
    "    input_dim = input.dimshuffle(0, 2, 3, 1)\n",
    "    input_transformed = _interpolate(\n",
    "        input_dim, x_s_flat, y_s_flat,\n",
    "        out_height, out_width)\n",
    "\n",
    "    output = T.reshape(\n",
    "        input_transformed, (num_batch, out_height, out_width, num_channels))\n",
    "    output = output.dimshuffle(0, 3, 1, 2)  # dimshuffle to conv format\n",
    "    return output, T_g\n",
    "#OUTPUT: T_g is also included.\n",
    "\n",
    "def _linspace(start, stop, num):\n",
    "    # Theano linspace. Behaves similar to np.linspace\n",
    "    start = T.cast(start, theano.config.floatX)\n",
    "    stop = T.cast(stop, theano.config.floatX)\n",
    "    num = T.cast(num, theano.config.floatX)\n",
    "    step = (stop-start)/(num-1)\n",
    "    return T.arange(num, dtype=theano.config.floatX)*step+start\n",
    "\n",
    "def _interpolate(im, x, y, out_height, out_width):\n",
    "    # *_f are floats\n",
    "    num_batch, height, width, channels = im.shape\n",
    "    height_f = T.cast(height, theano.config.floatX)\n",
    "    width_f = T.cast(width, theano.config.floatX)\n",
    "\n",
    "    # clip coordinates to [-1, 1]\n",
    "    x = T.clip(x, -1, 1)\n",
    "    y = T.clip(y, -1, 1)\n",
    "\n",
    "    # scale coordinates from [-1, 1] to [0, width/height - 1]\n",
    "    x = (x + 1) / 2 * (width_f - 1)\n",
    "    y = (y + 1) / 2 * (height_f - 1)\n",
    "\n",
    "    # obtain indices of the 2x2 pixel neighborhood surrounding the coordinates;\n",
    "    # we need those in floatX for interpolation and in int64 for indexing. for\n",
    "    # indexing, we need to take care they do not extend past the image.\n",
    "    x0_f = T.floor(x)\n",
    "    y0_f = T.floor(y)\n",
    "    x1_f = x0_f + 1\n",
    "    y1_f = y0_f + 1\n",
    "    x0 = T.cast(x0_f, 'int64')\n",
    "    y0 = T.cast(y0_f, 'int64')\n",
    "    x1 = T.cast(T.minimum(x1_f, width_f - 1), 'int64')\n",
    "    y1 = T.cast(T.minimum(y1_f, height_f - 1), 'int64')\n",
    "\n",
    "    # The input is [num_batch, height, width, channels]. We do the lookup in\n",
    "    # the flattened input, i.e [num_batch*height*width, channels]. We need\n",
    "    # to offset all indices to match the flat version\n",
    "    dim2 = width\n",
    "    dim1 = width*height\n",
    "    base = T.repeat(\n",
    "        T.arange(num_batch, dtype='int64')*dim1, out_height*out_width)\n",
    "    base_y0 = base + y0*dim2\n",
    "    base_y1 = base + y1*dim2\n",
    "    idx_a = base_y0 + x0\n",
    "    idx_b = base_y1 + x0\n",
    "    idx_c = base_y0 + x1\n",
    "    idx_d = base_y1 + x1\n",
    "\n",
    "    # use indices to lookup pixels for all samples\n",
    "    im_flat = im.reshape((-1, channels))\n",
    "    Ia = im_flat[idx_a]\n",
    "    Ib = im_flat[idx_b]\n",
    "    Ic = im_flat[idx_c]\n",
    "    Id = im_flat[idx_d]\n",
    "\n",
    "    # calculate interpolated values\n",
    "    wa = ((x1_f-x) * (y1_f-y)).dimshuffle(0, 'x')\n",
    "    wb = ((x1_f-x) * (y-y0_f)).dimshuffle(0, 'x')\n",
    "    wc = ((x-x0_f) * (y1_f-y)).dimshuffle(0, 'x')\n",
    "    wd = ((x-x0_f) * (y-y0_f)).dimshuffle(0, 'x')\n",
    "    output = T.sum([wa*Ia, wb*Ib, wc*Ic, wd*Id], axis=0)\n",
    "    return output\n"
   ]
  },
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   "source": [
    "class TransformerLayer8param(lasagne.layers.MergeLayer):\n",
    "    \"\"\"\n",
    "    Spatial transformer layer\n",
    "    The layer applies an bilinear transformation on the input. The bilinear\n",
    "    transformation is parameterized with six learned parameters [1]_.\n",
    "    The output is interpolated with a bilinear transformation.\n",
    "    Parameters\n",
    "    ----------\n",
    "    incoming : a :class:`Layer` instance or a tuple\n",
    "        The layer feeding into this layer, or the expected input shape. The\n",
    "        output of this layer should be a 4D tensor, with shape\n",
    "        ``(batch_size, num_input_channels, input_rows, input_columns)``.\n",
    "    localization_network : a :class:`Layer` instance\n",
    "        The network that calculates the parameters of the bilinear\n",
    "        transformation. See the example for how to initialize to the identity\n",
    "        transform.\n",
    "    downsample_factor : float or iterable of float\n",
    "        A float or a 2-element tuple specifying the downsample factor for the\n",
    "        output image (in both spatial dimensions). A value of 1 will keep the\n",
    "        original size of the input. Values larger than 1 will downsample the\n",
    "        input. Values below 1 will upsample the input.\n",
    "    References\n",
    "    ----------\n",
    "    .. [1]  Max Jaderberg, Karen Simonyan, Andrew Zisserman,\n",
    "            Koray Kavukcuoglu (2015):\n",
    "            Spatial Transformer Networks. NIPS 2015,\n",
    "            http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf\n",
    "    Examples\n",
    "    --------\n",
    "    Here we set up the layer to initially do the identity transform, similarly\n",
    "    to [1]_. Note that you will want to use a localization with linear output.\n",
    "    If the output from the localization networks is [t1, t2, t3, t4, t5, t6]\n",
    "    then t1 and t5 determines zoom, t2 and t4 determines skewness, and t3 and\n",
    "    t6 move the center position.\n",
    "    >>> import numpy as np\n",
    "    >>> import lasagne\n",
    "    >>> b = np.zeros((2, 4), dtype='float32')\n",
    "    >>> b[0, 0] = 1\n",
    "    >>> b[1, 1] = 1\n",
    "    >>> b = b.flatten()  # identity transform\n",
    "    >>> W = lasagne.init.Constant(0.0)\n",
    "    >>> l_in = lasagne.layers.InputLayer((None, 3, 28, 28))\n",
    "    >>> l_loc = lasagne.layers.DenseLayer(l_in, num_units=8, W=W, b=b,\n",
    "    ... nonlinearity=None)\n",
    "    >>> l_trans = lasagne.layers.TransformerLayer(l_in, l_loc)\n",
    "    \"\"\"\n",
    "    def __init__(self, incoming, localization_network, downsample_factor=1,\n",
    "                 **kwargs):\n",
    "        super(TransformerLayer8param, self).__init__(\n",
    "            [incoming, localization_network], **kwargs)\n",
    "        self.downsample_factor = as_tuple(downsample_factor, 2)\n",
    "\n",
    "        #self.y has been added.\n",
    "        input_shp, loc_shp = self.input_shapes\n",
    "\n",
    "        if loc_shp[-1] != 8 or len(loc_shp) != 2:\n",
    "            raise ValueError(\"The localization network must have \"\n",
    "                             \"output shape: (batch_size, 8)\")\n",
    "        if len(input_shp) != 4:\n",
    "            raise ValueError(\"The input network must have a 4-dimensional \"\n",
    "                             \"output shape: (batch_size, num_input_channels, \"\n",
    "                             \"input_rows, input_columns)\")\n",
    "\n",
    "    def get_output_shape_for(self, input_shapes):\n",
    "        shape = input_shapes[0]\n",
    "        factors = self.downsample_factor\n",
    "        return (shape[:2] + tuple(None if s is None else int(s // f)\n",
    "                                  for s, f in zip(shape[2:], factors)))\n",
    "\n",
    "    def get_output_for(self, inputs, **kwargs):\n",
    "        # see eq. (1) and sec 3.1 in [1]\n",
    "        input, theta = inputs\n",
    "        self.x, self.y = _transform_bilinear(theta, input, self.downsample_factor)\n",
    "        return self.x\n",
    "    \n",
    "def _meshgrid(height, width):\n",
    "    # This function is the grid generator from eq. (1) in reference [1].\n",
    "    # It is equivalent to the following numpy code:\n",
    "    #  x_t, y_t, xy_t = np.meshgrid(np.linspace(-1, 1, width),\n",
    "    #                         np.linspace(-1, 1, height))\n",
    "    #  ones = np.ones(np.prod(x_t.shape))\n",
    "    #  grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])\n",
    "    # It is implemented in Theano instead to support symbolic grid sizes.\n",
    "    # Note: If the image size is known at layer construction time, we could\n",
    "    # compute the meshgrid offline in numpy instead of doing it dynamically\n",
    "    # in Theano. However, it hardly affected performance when we tried.\n",
    "    x_t = T.dot(T.ones((height, 1)),\n",
    "                _linspace(-1.0, 1.0, width).dimshuffle('x', 0))\n",
    "    y_t = T.dot(_linspace(-1.0, 1.0, height).dimshuffle(0, 'x'),\n",
    "                T.ones((1, width)))\n",
    "    xy_t = x_t * y_t\n",
    "    \n",
    "    x_t_flat = x_t.reshape((1, -1))\n",
    "    y_t_flat = y_t.reshape((1, -1))\n",
    "    xy_t_flat = xy_t.reshape((1, -1))\n",
    "    \n",
    "    ones = T.ones_like(x_t_flat)\n",
    "    grid = T.concatenate([x_t_flat, y_t_flat, xy_t_flat, ones], axis=0)\n",
    "    return grid\n",
    "    \n",
    "def _transform_bilinear(theta, input, downsample_factor):\n",
    "    num_batch, num_channels, height, width = input.shape\n",
    "    theta = T.reshape(theta, (-1, 2, 4))\n",
    "\n",
    "    # grid of (x_t, y_t, 1), eq (1) in ref [1]\n",
    "    out_height = T.cast(height // downsample_factor[0], 'int64')\n",
    "    out_width = T.cast(width // downsample_factor[1], 'int64')\n",
    "    grid = _meshgrid(out_height, out_width)\n",
    "\n",
    "    # Transform A x (x_t, y_t, xy_t, 1)^T -> (x_s, y_s, xy_s)\n",
    "    T_g = T.dot(theta, grid)\n",
    "    x_s = T_g[:, 0]\n",
    "    y_s = T_g[:, 1]\n",
    "    x_s_flat = x_s.flatten()\n",
    "    y_s_flat = y_s.flatten()\n",
    "\n",
    "    # dimshuffle input to  (bs, height, width, channels)\n",
    "    input_dim = input.dimshuffle(0, 2, 3, 1)\n",
    "    input_transformed = _interpolate(\n",
    "        input_dim, x_s_flat, y_s_flat, \n",
    "        out_height, out_width)\n",
    "\n",
    "    output = T.reshape(\n",
    "        input_transformed, (num_batch, out_height, out_width, num_channels))\n",
    "    output = output.dimshuffle(0, 3, 1, 2)  # dimshuffle to conv format\n",
    "    return output, T_g\n",
    "#OUTPUT: T_g is also included.\n",
    "\n",
    "def _linspace(start, stop, num):\n",
    "    # Theano linspace. Behaves similar to np.linspace\n",
    "    start = T.cast(start, theano.config.floatX)\n",
    "    stop = T.cast(stop, theano.config.floatX)\n",
    "    num = T.cast(num, theano.config.floatX)\n",
    "    step = (stop-start)/(num-1)\n",
    "    return T.arange(num, dtype=theano.config.floatX)*step+start\n",
    "\n",
    "def _interpolate(im, x, y, out_height, out_width):\n",
    "    # *_f are floats\n",
    "    num_batch, height, width, channels = im.shape\n",
    "    height_f = T.cast(height, theano.config.floatX)\n",
    "    width_f = T.cast(width, theano.config.floatX)\n",
    "\n",
    "    # clip coordinates to [-1, 1]\n",
    "    x = T.clip(x, -1, 1)\n",
    "    y = T.clip(y, -1, 1)\n",
    "\n",
    "    # scale coordinates from [-1, 1] to [0, width/height - 1]\n",
    "    x = (x + 1) / 2 * (width_f - 1)\n",
    "    y = (y + 1) / 2 * (height_f - 1)\n",
    "\n",
    "    # obtain indices of the 2x2 pixel neighborhood surrounding the coordinates;\n",
    "    # we need those in floatX for interpolation and in int64 for indexing. for\n",
    "    # indexing, we need to take care they do not extend past the image.\n",
    "    x0_f = T.floor(x)\n",
    "    y0_f = T.floor(y)\n",
    "    x1_f = x0_f + 1\n",
    "    y1_f = y0_f + 1\n",
    "    x0 = T.cast(x0_f, 'int64')\n",
    "    y0 = T.cast(y0_f, 'int64')\n",
    "    x1 = T.cast(T.minimum(x1_f, width_f - 1), 'int64')\n",
    "    y1 = T.cast(T.minimum(y1_f, height_f - 1), 'int64')\n",
    "\n",
    "    # The input is [num_batch, height, width, channels]. We do the lookup in\n",
    "    # the flattened input, i.e [num_batch*height*width, channels]. We need\n",
    "    # to offset all indices to match the flat version\n",
    "    dim2 = width\n",
    "    dim1 = width*height\n",
    "    base = T.repeat(\n",
    "        T.arange(num_batch, dtype='int64')*dim1, out_height*out_width)\n",
    "    base_y0 = base + y0*dim2\n",
    "    base_y1 = base + y1*dim2\n",
    "    idx_a = base_y0 + x0\n",
    "    idx_b = base_y1 + x0\n",
    "    idx_c = base_y0 + x1\n",
    "    idx_d = base_y1 + x1\n",
    "\n",
    "    # use indices to lookup pixels for all samples\n",
    "    im_flat = im.reshape((-1, channels))\n",
    "    Ia = im_flat[idx_a]\n",
    "    Ib = im_flat[idx_b]\n",
    "    Ic = im_flat[idx_c]\n",
    "    Id = im_flat[idx_d]\n",
    "\n",
    "    # calculate interpolated values\n",
    "    wa = ((x1_f-x) * (y1_f-y)).dimshuffle(0, 'x')\n",
    "    wb = ((x1_f-x) * (y-y0_f)).dimshuffle(0, 'x')\n",
    "    wc = ((x-x0_f) * (y1_f-y)).dimshuffle(0, 'x')\n",
    "    wd = ((x-x0_f) * (y-y0_f)).dimshuffle(0, 'x')\n",
    "    output = T.sum([wa*Ia, wb*Ib, wc*Ic, wd*Id], axis=0)\n",
    "    return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: (150000, 1, 60, 60)\n",
      "Validation samples: (30000, 1, 60, 60)\n",
      "Test samples: (30000, 1, 60, 60)\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "# PARAMETERS\n",
    "t_start = time.time()\n",
    "test_dir = \"/home/dontgetdown/Documents/caner_grad/MNIST_IMAGES/45_90_derece/10k_10k_test\"\n",
    "train_dir = \"/home/dontgetdown/Documents/caner_grad/MNIST_IMAGES/45_90_derece/50k_50k_train\"\n",
    "out_file = \"mnist_cluttered_60x60_45_90degree_rotations.npz\"\n",
    "train_k = 5\n",
    "valid_k = 1\n",
    "n_classes = 10\n",
    "DIM = 60\n",
    "\n",
    "#COMMENTING STARTS IF THE FILE ALREADY EXISTS\n",
    "\n",
    "# test_file = open(test_dir, \"r\")\n",
    "# train_file = open(train_dir, \"r\")\n",
    "\n",
    "# test_file_lines = test_file.readlines()\n",
    "# test_images_loc = []\n",
    "# test_classes_pre = []\n",
    "# test_images_loc.append([test_file_lines[i].split(\" \")[0] for i in range(len(test_file_lines))])\n",
    "# test_classes_pre.append([test_file_lines[i].split(\" \")[1] for i in range(len(test_file_lines))])\n",
    "\n",
    "# trainandvalid_file_lines = train_file.readlines()\n",
    "# tv_size = len(trainandvalid_file_lines) \n",
    "# test_size = len(test_file_lines)\n",
    "\n",
    "# test_file.close()\n",
    "# train_file.close()\n",
    "\n",
    "# trainandvalid_images_loc = []\n",
    "# trainandvalid_classes_pre = []\n",
    "# trainandvalid_images_loc.append([trainandvalid_file_lines[i].split(\" \")[0] for i in range(tv_size)])\n",
    "# trainandvalid_classes_pre.append([trainandvalid_file_lines[i].split(\" \")[1] for i in range(tv_size)])\n",
    "\n",
    "# permuted_indexes = np.random.permutation(np.arange(len(trainandvalid_file_lines)))\n",
    "# train_indexes = permuted_indexes[:tv_size*train_k/(train_k+valid_k)]\n",
    "# valid_indexes = permuted_indexes[tv_size*train_k/(train_k+valid_k):tv_size]\n",
    "    \n",
    "# valid_classes_list = []\n",
    "# for i in xrange(tv_size*valid_k/(train_k+valid_k)):\n",
    "#     valid_classes_list.append(trainandvalid_classes_pre[0][valid_indexes[i]])\n",
    "\n",
    "# train_classes_list = []\n",
    "# for i in xrange(tv_size*train_k/(train_k+valid_k)):\n",
    "#     train_classes_list.append(trainandvalid_classes_pre[0][train_indexes[i]])\n",
    "    \n",
    "# test_classes = np.zeros((test_size, 10), dtype=np.int64)\n",
    "# for i in xrange(test_size):\n",
    "#     for j in range(n_classes):\n",
    "#         if int(test_classes_pre[0][i])==int(j):\n",
    "#             test_classes[i][j] = 1\n",
    "             \n",
    "# train_classes = np.zeros((tv_size*train_k/(valid_k+train_k), 10), dtype=np.int64)\n",
    "# for i in xrange(tv_size*train_k/(train_k+valid_k)):\n",
    "#     for j in xrange(n_classes):\n",
    "#         if(int(j)==int(train_classes_list[i])):\n",
    "#             train_classes[i][j] = 1 \n",
    "            \n",
    "# valid_classes = np.zeros((tv_size*valid_k/(valid_k+train_k), 10), dtype=np.int64)\n",
    "# for i in xrange(tv_size*valid_k/(train_k+valid_k)):\n",
    "#     for j in xrange(n_classes):\n",
    "#         if(int(j)==int(valid_classes_list[i])):\n",
    "#             valid_classes[i][j] = 1 \n",
    "\n",
    "# t_cl_end = time.time()\n",
    "# print t_cl_end - t_start\n",
    "\n",
    "# test_images = np.zeros((test_size, 1, DIM, DIM), dtype=np.float32)\n",
    "# train_images = np.zeros((tv_size*train_k/(valid_k+train_k), 1, DIM, DIM), dtype=np.float32)\n",
    "# valid_images = np.zeros((tv_size*valid_k/(valid_k+train_k), 1, DIM, DIM), dtype=np.float32)\n",
    "\n",
    "# for i in xrange(test_images.shape[0]):\n",
    "#     test_images[i] = cv2.imread(test_images_loc[0][i], cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "# for i in xrange(train_images.shape[0]):\n",
    "#     train_images[i] = cv2.imread(trainandvalid_images_loc[0][train_indexes[i]], cv2.IMREAD_GRAYSCALE)\n",
    "    \n",
    "# for i in xrange(valid_images.shape[0]):\n",
    "#     valid_images[i] = cv2.imread(trainandvalid_images_loc[0][valid_indexes[i]], cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "# valid_images[:] = (valid_images - 128)/(128+0.0000001)\n",
    "# test_images[:] = (test_images - 128)/(128+0.0000001)\n",
    "# train_images[:] = (train_images - 128)/(128+0.0000001)\n",
    "\n",
    "\n",
    "# dictionary = {'x_train':train_images, 'x_valid':valid_images, 'x_test':test_images, 'y_train':train_classes, \n",
    "#               'y_valid':valid_classes, 'y_test':test_classes}\n",
    "\n",
    "# t_dict_end = time.time()\n",
    "# print t_dict_end - t_cl_end\n",
    "\n",
    "# np.savez(out_file, x_train=dictionary['x_train'], y_train=dictionary['y_train'], x_test=dictionary['x_test'], y_test=dictionary['y_test'], x_valid=dictionary['x_valid'], y_valid=dictionary['y_valid']) \n",
    "# t_save_end = time.time()\n",
    "# print t_save_end - t_dict_end \n",
    "\n",
    "# COMMENTING END IF THE FILE ALREADY EXISTS\n",
    "\n",
    "mnist_90_rotated = out_file\n",
    "#THEN, BY USING A DIFFERENT NUMPY ARRAY DICTIONARY, WE WILL BE LOADING OUR DATA.\n",
    "def load_data(x):\n",
    "    data = np.load(x)\n",
    "    \n",
    "    X_train, y_train = data['x_train'], np.argmax(data['y_train'], axis=-1)\n",
    "    X_valid, y_valid = data['x_valid'], np.argmax(data['y_valid'], axis=-1)\n",
    "    X_test, y_test = data['x_test'], np.argmax(data['y_test'], axis=-1)\n",
    "\n",
    "    # reshape for convolutions\n",
    "    X_train = X_train.reshape((X_train.shape[0], 1, DIM, DIM))\n",
    "    X_valid = X_valid.reshape((X_valid.shape[0], 1, DIM, DIM))\n",
    "    X_test = X_test.reshape((X_test.shape[0], 1, DIM, DIM))\n",
    "    \n",
    "    print \"Train samples:\", X_train.shape\n",
    "    print \"Validation samples:\", X_valid.shape\n",
    "    print \"Test samples:\", X_test.shape\n",
    "\n",
    "    return dict(\n",
    "        X_train=lasagne.utils.floatX(X_train),\n",
    "        y_train=y_train.astype('int32'),\n",
    "        X_valid=lasagne.utils.floatX(X_valid),\n",
    "        y_valid=y_valid.astype('int32'),\n",
    "        X_test=lasagne.utils.floatX(X_test),\n",
    "        y_test=y_test.astype('int32'),\n",
    "        num_examples_train=X_train.shape[0],\n",
    "        num_examples_valid=X_valid.shape[0],\n",
    "        num_examples_test=X_test.shape[0],\n",
    "        input_height=X_train.shape[2],\n",
    "        input_width=X_train.shape[3],\n",
    "        output_dim=10,)\n",
    "data = load_data(mnist_90_rotated)\n",
    "# print time.time() - t_save_end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  ..., \n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]]]\n",
      "[[[-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  ..., \n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]\n",
      "  [-1. -1. -1. ..., -1. -1. -1.]]]\n"
     ]
    }
   ],
   "source": [
    "#For seeing if the data is accessable in a valid form\n",
    "\n",
    "print data['X_valid'][101]\n",
    "print data['X_train'][101]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IgFZCBEArIQKglRAB0EqIAGglRAC0EiIAWgkRAK2ECIBWQgRAKyECoJUQAdBKiABoJUQA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\nAqDV/wFFuAUWzSazTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5808c1e450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To see if there are any problems with the image\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.imshow(data['X_test'][1010].reshape(DIM, DIM), cmap='gray', interpolation='none')\n",
    "plt.title('Cluttered MNIST', fontsize=20)\n",
    "plt.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformer network output shape:  (None, 1, 20, 20)\n"
     ]
    }
   ],
   "source": [
    "# Model File\n",
    "\n",
    "def build_model(input_width, input_height, output_dim,\n",
    "                batch_size=BATCH_SIZE):\n",
    "    ini = lasagne.init.HeUniform()\n",
    "    l_in = lasagne.layers.InputLayer(shape=(None, 1, input_width, input_height),)\n",
    "\n",
    "    # Localization network\n",
    "    b = np.zeros((2, 4), dtype=theano.config.floatX)\n",
    "    b[0, 0] = 1\n",
    "    b[1, 1] = 1\n",
    "    b = b.flatten()\n",
    "    loc_l1 = pool(l_in, pool_size=(2, 2))\n",
    "    loc_l2 = conv(\n",
    "        loc_l1, num_filters=20, filter_size=(5, 5), W=ini)\n",
    "    loc_l3 = pool(loc_l2, pool_size=(2, 2))\n",
    "    loc_l4 = conv(loc_l3, num_filters=20, filter_size=(5, 5), W=ini)\n",
    "    loc_l5 = lasagne.layers.DenseLayer(\n",
    "        loc_l4, num_units=50, W=lasagne.init.HeUniform('relu'))\n",
    "    loc_out = lasagne.layers.DenseLayer(\n",
    "        loc_l5, num_units=8, b=b, W=lasagne.init.Constant(0.0), \n",
    "        nonlinearity=lasagne.nonlinearities.identity)\n",
    "    \n",
    "    # Transformer network\n",
    "    l_trans1 = TransformerLayer8param(l_in, loc_out, downsample_factor=3.0)\n",
    "    print \"Transformer network output shape: \", l_trans1.output_shape\n",
    "    \n",
    "    # Classification network\n",
    "    class_l1 = conv(\n",
    "        l_trans1,\n",
    "        num_filters=32,\n",
    "        filter_size=(3, 3),\n",
    "        nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        W=ini,\n",
    "    )\n",
    "    class_l2 = pool(class_l1, pool_size=(2, 2))\n",
    "    class_l3 = conv(\n",
    "        class_l2,\n",
    "        num_filters=32,\n",
    "        filter_size=(3, 3),\n",
    "        nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        W=ini,\n",
    "    )\n",
    "    class_l4 = pool(class_l3, pool_size=(2, 2))\n",
    "    class_l5 = lasagne.layers.DenseLayer(\n",
    "        class_l4,\n",
    "        num_units=256,\n",
    "        nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        W=ini,\n",
    "    )\n",
    "\n",
    "    l_out = lasagne.layers.DenseLayer(\n",
    "        class_l5,\n",
    "        num_units=output_dim,\n",
    "        nonlinearity=lasagne.nonlinearities.softmax,\n",
    "        W=ini,\n",
    "    )\n",
    "\n",
    "    return l_out, l_trans1, loc_out\n",
    "\n",
    "# model --> network model\n",
    "# l_transform --> the output of the transformer layer\n",
    "# thetas --> output of the localizer block \n",
    "\n",
    "model, l_transform, thetas = build_model(DIM, DIM, NUM_CLASSES)\n",
    "model_params = lasagne.layers.get_all_params(model, trainable=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/lasagne/layers/conv.py:489: UserWarning: The `image_shape` keyword argument to `tensor.nnet.conv2d` is deprecated, it has been renamed to `input_shape`.\n",
      "  border_mode=border_mode)\n",
      "WARNING (theano.tensor.blas): We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n"
     ]
    }
   ],
   "source": [
    "# train and eval functions\n",
    "\n",
    "X = T.tensor4()\n",
    "y = T.ivector()\n",
    "\n",
    "# training output\n",
    "output_train = lasagne.layers.get_output(model, X, deterministic=False)\n",
    "\n",
    "# evaluation output. Also includes output of transform for plotting\n",
    "output_eval, transform_eval, thetas_tensor = lasagne.layers.get_output([model, l_transform, thetas], X, deterministic=True)\n",
    "\n",
    "sh_lr = theano.shared(lasagne.utils.floatX(LEARNING_RATE))\n",
    "cost = T.mean(T.nnet.categorical_crossentropy(output_train, y))\n",
    "cost_test = T.mean(T.nnet.categorical_crossentropy(output_eval, y))\n",
    "updates = lasagne.updates.adam(cost, model_params, learning_rate=sh_lr)\n",
    "\n",
    "train = theano.function([X, y], [cost, output_train], updates=updates)\n",
    "eval = theano.function([X, y], [output_eval, transform_eval, thetas_tensor, cost_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Mini-batch SGD and Forward pass modules\n",
    "\n",
    "def train_epoch(X, y):\n",
    "    num_samples = X.shape[0]\n",
    "    num_batches = int(np.ceil(num_samples / float(BATCH_SIZE)))\n",
    "    loss = []\n",
    "    correct = 0\n",
    "    for i in range(num_batches):\n",
    "        idx = range(i*BATCH_SIZE, np.minimum((i+1)*BATCH_SIZE, num_samples))\n",
    "        X_batch = X[idx]\n",
    "        y_batch = y[idx]\n",
    "        cost_batch, output_train = train(X_batch, y_batch)\n",
    "        loss += [cost_batch]\n",
    "        preds = np.argmax(output_train, axis=-1)\n",
    "        correct += np.sum(y_batch == preds)\n",
    "\n",
    "    return np.mean(loss), correct / float(num_samples)\n",
    "\n",
    "\n",
    "def eval_epoch(X, y):\n",
    "    loss = []\n",
    "    output_eval, transform_eval, actual_thetas, loss = eval(X, y)\n",
    "    preds = np.argmax(output_eval, axis=-1)\n",
    "    acc = np.mean(preds == y)\n",
    "    return np.mean(loss), acc, transform_eval, actual_thetas, preds, output_eval\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#EDITS SHOULD BE PERFORMED\n",
    "# BEFORE TRAINING, HOW IS THE ACCURACY LIKE?\n",
    "# y_test2 = np.array([1, 1, 1, 1, 4, 4, 4, 4, 7, 0, 7, 2, 2, 2, 0, 0, 0, 2, 7, 7, 3, 3, 3, 3, 6, 6, 5, 5, 5, 5, 8, 6, 8, 6, 8, 8, 9, 9, 9, 9], dtype=np.int32)\n",
    "# X_test2 = np.zeros((y_test2.shape[0], 1, 60, 60), dtype=np.float32)\n",
    "# for i in range(40):\n",
    "#     X_test2[i] = cv2.imread(\"/home/dl2/caner_grad/temp/translated_ones/\"+str(i)+ \".png\", cv2.IMREAD_GRAYSCALE)\n",
    "# for i in range(40):    \n",
    "#     X_test2[i] = (X_test2[i] - np.mean(X_test2[i]))/np.std(X_test2[i])\n",
    "# test_loss2, test_acc2, test_transform2, actual_thetas2, preds2, output_eval2 = eval_epoch(X_test2, y_test2)\n",
    "# print \"test loss {0}, test acc {1}\".format(test_loss2, test_acc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(7,28))\n",
    "# k=20\n",
    "# for i in range(k):\n",
    "#     plt.subplot(k,2,1+i*2)\n",
    "#     plt.imshow(X_test2[i].reshape(DIM, DIM), cmap='gray', interpolation='none')\n",
    "#     if i == 0:\n",
    "#         plt.title('Original 60x60', fontsize=20)\n",
    "#     plt.axis('off')\n",
    "#     plt.subplot(k,2,2+i*2)\n",
    "#     plt.imshow(test_transform2[i].reshape(DIM//3, DIM//3), cmap='gray', interpolation='none')\n",
    "#     if i == 0:\n",
    "#         plt.title('Transformed 20x20', fontsize=20)\n",
    "#     plt.axis('off')\n",
    "# plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Train loss 1.08239138126, Train acc 0.634666666667, val acc 0.889833333333\n",
      "Epoch 1: Train loss 0.260918796062, Train acc 0.918486666667, val acc 0.944866666667\n",
      "Epoch 2: Train loss 0.157917439938, Train acc 0.9506, val acc 0.959433333333\n",
      "Epoch 3: Train loss 0.123279079795, Train acc 0.96114, val acc 0.968066666667\n",
      "Epoch 4: Train loss 0.108974784613, Train acc 0.965646666667, val acc 0.970333333333\n",
      "Epoch 5: Train loss 0.0960087701678, Train acc 0.96916, val acc 0.973\n",
      "Epoch 6: Train loss 0.0865200161934, Train acc 0.972786666667, val acc 0.9683\n",
      "Epoch 7: Train loss 0.076320566237, Train acc 0.976126666667, val acc 0.978666666667\n",
      "Epoch 8: Train loss 0.0693063288927, Train acc 0.97826, val acc 0.978233333333\n",
      "Epoch 9: Train loss 0.0743781626225, Train acc 0.97644, val acc 0.972366666667\n",
      "Epoch 10: Train loss 0.0663107261062, Train acc 0.97898, val acc 0.976166666667\n",
      "Epoch 11: Train loss 0.0632852315903, Train acc 0.979893333333, val acc 0.971333333333\n",
      "Epoch 12: Train loss 0.0635208338499, Train acc 0.979886666667, val acc 0.978733333333\n",
      "Epoch 13: Train loss 0.0572893694043, Train acc 0.981366666667, val acc 0.982566666667\n",
      "Epoch 14: Train loss 0.0544410161674, Train acc 0.982533333333, val acc 0.9817\n",
      "Epoch 15: Train loss 0.0533019267023, Train acc 0.983186666667, val acc 0.978266666667\n",
      "Epoch 16: Train loss 0.0514704659581, Train acc 0.98344, val acc 0.981633333333\n",
      "Epoch 17: Train loss 0.053696513176, Train acc 0.98252, val acc 0.9765\n",
      "Epoch 18: Train loss 0.0519110262394, Train acc 0.983513333333, val acc 0.983333333333\n",
      "New LR: 0.000700000033248\n",
      "Epoch 19: Train loss 0.0487811863422, Train acc 0.98402, val acc 0.979033333333\n",
      "Epoch 20: Train loss 0.038221322, Train acc 0.9876, val acc 0.986033333333\n",
      "Epoch 21: Train loss 0.0372156500816, Train acc 0.988, val acc 0.9841\n",
      "Epoch 22: Train loss 0.0356912612915, Train acc 0.988426666667, val acc 0.984533333333\n",
      "Epoch 23: Train loss 0.0365019068122, Train acc 0.988226666667, val acc 0.9861\n",
      "Epoch 24: Train loss 0.0335937924683, Train acc 0.98906, val acc 0.984933333333\n",
      "Epoch 25: Train loss 0.0323573723435, Train acc 0.989466666667, val acc 0.986766666667\n",
      "Epoch 26: Train loss 0.0334281250834, Train acc 0.98896, val acc 0.9864\n",
      "Epoch 27: Train loss 0.030734827742, Train acc 0.98996, val acc 0.984433333333\n",
      "Epoch 28: Train loss 0.0289568956941, Train acc 0.99036, val acc 0.984733333333\n",
      "Epoch 29: Train loss 0.0292502027005, Train acc 0.990393333333, val acc 0.985766666667\n",
      "Epoch 30: Train loss 0.031246881932, Train acc 0.989693333333, val acc 0.9858\n",
      "Epoch 31: Train loss 0.0269324146211, Train acc 0.99092, val acc 0.985466666667\n",
      "Epoch 32: Train loss 0.0280860271305, Train acc 0.99088, val acc 0.986\n",
      "Epoch 33: Train loss 0.0271515976638, Train acc 0.990866666667, val acc 0.9859\n",
      "Epoch 34: Train loss 0.0270948037505, Train acc 0.991066666667, val acc 0.9856\n",
      "Epoch 35: Train loss 0.0232171546668, Train acc 0.992266666667, val acc 0.986366666667\n",
      "Epoch 36: Train loss 0.0249415989965, Train acc 0.99186, val acc 0.985333333333\n",
      "Epoch 37: Train loss 0.0254802331328, Train acc 0.99162, val acc 0.9832\n",
      "Epoch 38: Train loss 0.0245580375195, Train acc 0.991973333333, val acc 0.987533333333\n",
      "New LR: 0.000490000023274\n",
      "Epoch 39: Train loss 0.02396363765, Train acc 0.99222, val acc 0.986033333333\n",
      "Epoch 40: Train loss 0.0173954982311, Train acc 0.9945, val acc 0.9878\n",
      "Epoch 41: Train loss 0.0161555428058, Train acc 0.994946666667, val acc 0.9881\n",
      "Epoch 42: Train loss 0.0154991298914, Train acc 0.994733333333, val acc 0.9869\n",
      "Epoch 43: Train loss 0.0183627288789, Train acc 0.994086666667, val acc 0.987633333333\n",
      "Epoch 44: Train loss 0.0143204629421, Train acc 0.995326666667, val acc 0.987533333333\n",
      "Epoch 45: Train loss 0.016334226355, Train acc 0.994726666667, val acc 0.9873\n",
      "Epoch 46: Train loss 0.0134754963219, Train acc 0.995633333333, val acc 0.986666666667\n",
      "Epoch 47: Train loss 0.0138987163082, Train acc 0.99548, val acc 0.987166666667\n",
      "Epoch 48: Train loss 0.0125953154638, Train acc 0.995873333333, val acc 0.987766666667\n",
      "Epoch 49: Train loss 0.0140107888728, Train acc 0.995413333333, val acc 0.987033333333\n",
      "Epoch 50: Train loss 0.0139244096354, Train acc 0.99526, val acc 0.987933333333\n",
      "Epoch 51: Train loss 0.0127526549622, Train acc 0.995606666667, val acc 0.988666666667\n",
      "Epoch 52: Train loss 0.0112597215921, Train acc 0.996253333333, val acc 0.9883\n",
      "Epoch 53: Train loss 0.0118072591722, Train acc 0.996006666667, val acc 0.986833333333\n",
      "Epoch 54: Train loss 0.010421092622, Train acc 0.996633333333, val acc 0.9869\n",
      "Epoch 55: Train loss 0.0114226387814, Train acc 0.996146666667, val acc 0.986866666667\n",
      "Epoch 56: Train loss 0.0147285461426, Train acc 0.995226666667, val acc 0.987666666667\n",
      "Epoch 57: Train loss 0.0132876895368, Train acc 0.995673333333, val acc 0.988233333333\n",
      "Epoch 58: Train loss 0.00915311370045, Train acc 0.997066666667, val acc 0.987566666667\n",
      "New LR: 0.000343000004068\n",
      "Epoch 59: Train loss 0.0138868549839, Train acc 0.995366666667, val acc 0.9878\n",
      "Epoch 60: Train loss 0.0079625332728, Train acc 0.997373333333, val acc 0.989\n",
      "Epoch 61: Train loss 0.0069167483598, Train acc 0.997686666667, val acc 0.988866666667\n",
      "Epoch 62: Train loss 0.00611528288573, Train acc 0.998113333333, val acc 0.988666666667\n",
      "Epoch 63: Train loss 0.00582780689001, Train acc 0.997973333333, val acc 0.9881\n",
      "Epoch 64: Train loss 0.00576490350068, Train acc 0.997993333333, val acc 0.988666666667\n",
      "Epoch 65: Train loss 0.00777690624818, Train acc 0.997286666667, val acc 0.9894\n",
      "Epoch 66: Train loss 0.00476375129074, Train acc 0.99846, val acc 0.987833333333\n",
      "Epoch 67: Train loss 0.006041593384, Train acc 0.99794, val acc 0.9891\n",
      "Epoch 68: Train loss 0.0064820474945, Train acc 0.997746666667, val acc 0.989333333333\n",
      "Epoch 69: Train loss 0.00557860312983, Train acc 0.998306666667, val acc 0.9892\n",
      "Epoch 70: Train loss 0.00534948240966, Train acc 0.998346666667, val acc 0.99\n",
      "Epoch 71: Train loss 0.00490090809762, Train acc 0.998493333333, val acc 0.988666666667\n",
      "Epoch 72: Train loss 0.00550958001986, Train acc 0.9982, val acc 0.988633333333\n",
      "Epoch 73: Train loss 0.00533438194543, Train acc 0.998233333333, val acc 0.988533333333\n",
      "Epoch 74: Train loss 0.0049984715879, Train acc 0.998386666667, val acc 0.988533333333\n",
      "Epoch 75: Train loss 0.00453558750451, Train acc 0.998546666667, val acc 0.9882\n",
      "Epoch 76: Train loss 0.00545354234055, Train acc 0.998273333333, val acc 0.9889\n",
      "Epoch 77: Train loss 0.00502728763968, Train acc 0.998333333333, val acc 0.9871\n",
      "Epoch 78: Train loss 0.00493565946817, Train acc 0.99828, val acc 0.989066666667\n",
      "New LR: 0.000240099994699\n",
      "Epoch 79: Train loss 0.00674451841041, Train acc 0.99782, val acc 0.9886\n",
      "Epoch 80: Train loss 0.00291976938024, Train acc 0.999053333333, val acc 0.989666666667\n",
      "Epoch 81: Train loss 0.00210281903856, Train acc 0.999306666667, val acc 0.9897\n",
      "Epoch 82: Train loss 0.00203443970531, Train acc 0.99938, val acc 0.988466666667\n",
      "Epoch 83: Train loss 0.00268485327251, Train acc 0.999193333333, val acc 0.989466666667\n",
      "Epoch 84: Train loss 0.0020829574205, Train acc 0.999386666667, val acc 0.989066666667\n",
      "Epoch 85: Train loss 0.00294750975445, Train acc 0.99916, val acc 0.988933333333\n",
      "Epoch 86: Train loss 0.00261961272918, Train acc 0.999146666667, val acc 0.989766666667\n",
      "Epoch 87: Train loss 0.00195887754671, Train acc 0.999413333333, val acc 0.988933333333\n",
      "Epoch 88: Train loss 0.00210900558159, Train acc 0.99938, val acc 0.988566666667\n",
      "Epoch 89: Train loss 0.00174409861211, Train acc 0.999486666667, val acc 0.989533333333\n",
      "Epoch 90: Train loss 0.00174854882061, Train acc 0.99952, val acc 0.988666666667\n",
      "Epoch 91: Train loss 0.00131608475931, Train acc 0.999633333333, val acc 0.989033333333\n",
      "Epoch 92: Train loss 0.00177428894676, Train acc 0.99946, val acc 0.989166666667\n",
      "Epoch 93: Train loss 0.00213685119525, Train acc 0.999333333333, val acc 0.9889\n",
      "Epoch 94: Train loss 0.00305109587498, Train acc 0.999093333333, val acc 0.988966666667\n",
      "Epoch 95: Train loss 0.00215986510739, Train acc 0.999346666667, val acc 0.989366666667\n",
      "Epoch 96: Train loss 0.00173095182981, Train acc 0.999446666667, val acc 0.989233333333\n",
      "Epoch 97: Train loss 0.00120215979405, Train acc 0.999686666667, val acc 0.9895\n",
      "Epoch 98: Train loss 0.000933339586481, Train acc 0.999806666667, val acc 0.988566666667\n",
      "New LR: 0.000168069996289\n",
      "Epoch 99: Train loss 0.00198646006174, Train acc 0.999333333333, val acc 0.987266666667\n",
      "Epoch 100: Train loss 0.00169234920759, Train acc 0.999413333333, val acc 0.9889\n"
     ]
    }
   ],
   "source": [
    "# Training\n",
    "# SKIP IF THE NETWORK HAS BEEN PRETRAINED\n",
    "theano.config.exception_verbosity=\"high\"\n",
    "\n",
    "valid_accs, train_accs = [], []\n",
    "valid_losses, train_losses = [], []\n",
    "test_accs = []\n",
    "test_losses = []\n",
    "\n",
    "try:\n",
    "    for n in range(NUM_EPOCHS):\n",
    "        train_loss, train_acc = train_epoch(data['X_train'], data['y_train'])\n",
    "        val_loss, valid_acc, valid_trainsform, dummy, preds_valid, output_eval_valid  = eval_epoch(data['X_valid'], data['y_valid'])\n",
    "        valid_accs += [valid_acc]\n",
    "        train_accs += [train_acc]\n",
    "        valid_losses += [val_loss]\n",
    "        train_losses += [train_loss]\n",
    "        \n",
    "        np.savez('model'+str(n)+'.npz', *lasagne.layers.get_all_param_values(model))\n",
    "\n",
    "        if (n+1) % 20 == 0:\n",
    "            new_lr = sh_lr.get_value() * 0.7\n",
    "            print \"New LR:\", new_lr\n",
    "            sh_lr.set_value(lasagne.utils.floatX(new_lr))\n",
    "\n",
    "#         print \"Epoch {0}: Train loss {1}, Train acc {2}, val acc {3}, test acc {4}\".format(\n",
    "#                 n, train_loss, train_acc, valid_acc, test_acc) \n",
    "        print \"Epoch {0}: Train loss {1}, Train acc {2}, val acc {3}\".format(\n",
    "                n, train_loss, train_acc, valid_acc)\n",
    "#         print \"Epoch {0}: Train loss {1}, Train acc {2}\".format(\n",
    "#                 n, train_loss, train_acc)\n",
    "except KeyboardInterrupt:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "test_accs = []\n",
    "test_losses = []\n",
    "preds_train = []\n",
    "\n",
    "\n",
    "theano.config.exception_verbosity=\"high\"\n",
    "for i in range(101):\n",
    "    with np.load('/home/dontgetdown/Documents/caner_grad/rotated_45_90_code/8_thetas/model'+str(i)+\".npz\") as f:\n",
    "         param_values = [f['arr_%d' % i] for i in range(len(f.files))]\n",
    "    lasagne.layers.set_all_param_values(model, param_values)\n",
    "    test_loss, test_acc, test_transform, actual_thetas, preds, output_eval = eval_epoch(data['X_test'][:], data['y_test'][:])\n",
    "    test_accs += [test_acc]\n",
    "    test_losses += [test_loss]\n",
    "    preds_train += [np.argmax(output_eval, axis=-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.33007812, 0.1668347, 0.1139791, 0.091830015, 0.083576471, 0.079677552, 0.0943041, 0.060953531, 0.064785235, 0.08350049, 0.06779182, 0.078860335, 0.065346323, 0.050770018, 0.053849868, 0.067745596, 0.052782092, 0.069082923, 0.049465738, 0.059031658, 0.045050506, 0.047574844, 0.044143982, 0.043452531, 0.044623081, 0.04374034, 0.042675737, 0.047558062, 0.047276106, 0.043764059, 0.041369569, 0.049303576, 0.041322634, 0.043444939, 0.045947537, 0.045844242, 0.04887525, 0.051397119, 0.040981729, 0.047271822, 0.039638117, 0.04271622, 0.041438501, 0.040978126, 0.043418702, 0.04206609, 0.045276381, 0.042791758, 0.043067083, 0.043924488, 0.041908089, 0.03985161, 0.039396737, 0.046060361, 0.046633892, 0.048795775, 0.044966005, 0.04311667, 0.046241667, 0.039650522, 0.041530102, 0.042875472, 0.045631111, 0.041027881, 0.043356411, 0.043314647, 0.043352898, 0.044349387, 0.041528981, 0.044656783, 0.042326476, 0.044398535, 0.050753478, 0.049399272, 0.046311099, 0.047339559, 0.049596131, 0.053780437, 0.047895446, 0.044851888, 0.042699244, 0.044933438, 0.042619426, 0.045102693, 0.048429336, 0.045273162, 0.044510853, 0.044738311, 0.052602768, 0.049221937, 0.052626204, 0.052614648, 0.052159034, 0.053171031, 0.050402753, 0.05497412, 0.049788974, 0.047536649, 0.052548319, 0.063016593, 0.050610963]\n",
      "[0.8982, 0.9476, 0.96346666666666669, 0.97073333333333334, 0.97350000000000003, 0.97453333333333336, 0.96976666666666667, 0.97950000000000004, 0.97973333333333334, 0.97243333333333337, 0.97850000000000004, 0.9748, 0.97943333333333338, 0.98426666666666662, 0.98233333333333328, 0.97960000000000003, 0.98293333333333333, 0.97873333333333334, 0.98423333333333329, 0.98143333333333338, 0.9862333333333333, 0.98533333333333328, 0.98633333333333328, 0.98663333333333336, 0.98636666666666661, 0.98663333333333336, 0.98706666666666665, 0.98606666666666665, 0.98533333333333328, 0.98663333333333336, 0.98763333333333336, 0.98603333333333332, 0.98776666666666668, 0.98713333333333331, 0.98586666666666667, 0.98653333333333337, 0.98573333333333335, 0.98453333333333337, 0.98799999999999999, 0.98626666666666662, 0.98836666666666662, 0.98719999999999997, 0.98763333333333336, 0.98803333333333332, 0.98760000000000003, 0.98809999999999998, 0.98763333333333336, 0.98916666666666664, 0.98750000000000004, 0.98760000000000003, 0.9879, 0.98833333333333329, 0.98860000000000003, 0.98746666666666671, 0.98713333333333331, 0.98773333333333335, 0.98806666666666665, 0.98839999999999995, 0.98780000000000001, 0.98980000000000001, 0.9892333333333333, 0.98893333333333333, 0.98833333333333329, 0.98903333333333332, 0.98870000000000002, 0.98870000000000002, 0.98893333333333333, 0.98880000000000001, 0.98953333333333338, 0.98916666666666664, 0.98916666666666664, 0.98946666666666672, 0.98809999999999998, 0.98806666666666665, 0.98913333333333331, 0.98893333333333333, 0.9882333333333333, 0.98703333333333332, 0.9892333333333333, 0.98893333333333333, 0.98929999999999996, 0.98980000000000001, 0.98980000000000001, 0.98966666666666669, 0.98843333333333339, 0.98960000000000004, 0.98976666666666668, 0.98946666666666672, 0.98899999999999999, 0.98943333333333339, 0.98903333333333332, 0.98903333333333332, 0.99013333333333331, 0.98876666666666668, 0.9892333333333333, 0.9889, 0.98929999999999996, 0.98999999999999999, 0.98953333333333338, 0.98673333333333335, 0.98936666666666662]\n"
     ]
    }
   ],
   "source": [
    "#TEST ACC & LOSS FOR MODELS \n",
    "print test_losses\n",
    "print test_accs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ERCTmRP0cpnNuCjCl0LShAT93K2a9/wKtyra64Dxq+hERiTnRPiRb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\nSqkZ+P/d7m8BAhBqhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e4407a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IyHf5QKmy1pbJV8uWLe2FmrVmj63x5HQbv+vIBW9LnGHt2rVlPYWLzssvv2wB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\n4ejgpWmapk04OnhpmqZpE87/B9NwyPQvGhruAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e44019d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.title('Accuracies', fontsize=20)\n",
    "plt.plot(1-np.array(train_accs), label='Training Error')\n",
    "plt.plot(1-np.array(valid_accs), label='Validation Error')\n",
    "plt.plot(1-np.array(test_accs), label='Testing Error')\n",
    "plt.legend(fontsize=20)\n",
    "plt.xlabel('Epoch', fontsize=20)\n",
    "plt.ylabel('Error', fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.plot(range(len(train_losses)), train_losses, label='Training Loss')\n",
    "plt.plot(range(len(valid_losses)), valid_losses, label='Validation Loss')\n",
    "plt.plot(range(len(test_losses)), test_losses, label='Testing Loss')\n",
    "plt.xlabel('Epoch', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "plt.title('Train Loss', fontsize=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57269f24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Spatial Transformer I/O\n",
    "plt.figure(figsize=(7,14))\n",
    "k=1\n",
    "l = 23305\n",
    "for i in [l+j for j in range(k)]:\n",
    "    plt.subplot(k,2,1+(i-l)*2)\n",
    "    plt.imshow(data['X_test'][i].reshape(DIM, DIM), cmap='gray', interpolation='none')\n",
    "    if i == 0:\n",
    "        plt.title('Original 60x60', fontsize=20)\n",
    "    plt.axis('off')\n",
    "    plt.subplot(k,2,2+(i-l)*2)\n",
    "    plt.imshow(test_transform[i].reshape(DIM//3, DIM//3), cmap='gray', interpolation='none')\n",
    "    if i == 0:\n",
    "        plt.title('Transformed 20x20', fontsize=20)\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "print np.argmax(output_eval, axis=1)[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.51907754 -0.00361293 -0.0083427  -0.21191558  0.02880955  0.51611298\n",
      "  -0.00879138  0.2763072 ]]\n"
     ]
    }
   ],
   "source": [
    "print actual_thetas[l:l+k] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap              )\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=60)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float16') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, np.around(cm[i, j],decimals = 3),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize = (7,7))\n",
    "y_true = data['y_test'][20000:30000]\n",
    "y_pred = np.argmax(output_eval, axis=1)[20000:30000]\n",
    "\n",
    "cnf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "class_names = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize = (7,7))\n",
    "\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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