convert_theano_model_to_tf_model.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eramamur/bin/miniconda3/envs/gpu_keras_theano/lib/python3.6/site-packages/keras/utils/conv_utils.py:82: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.copy(kernel[slices])\n"
     ]
    }
   ],
   "source": [
    "microglia_classifier = load_model(\"/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/ml_gosselin_microglia/classification_model_8.hdf5\", compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "microglia_classifier.save(\"/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/ml_gosselin_microglia/classification_model_8_tf.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eramamur/bin/miniconda3/envs/gpu_keras_theano/lib/python3.6/site-packages/keras/utils/conv_utils.py:82: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.copy(kernel[slices])\n"
     ]
    }
   ],
   "source": [
    "neuron_classifier = load_model(\"/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/ml_fullard_neun/classification_model_4.hdf5\", compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "neuron_classifier.save(\"/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/ml_fullard_neun/classification_model_4_tf.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "monocyte_classifier = load_model(\"/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/ml_monocyte/classification_model_4.hdf5\", compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "monocyte_classifier.save(\"/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/ml_monocyte/classification_model_4_tf.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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