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  {
   "cell_type": "markdown",
   "id": "3b921d47-d760-4438-b754-8a6d805e9415",
   "metadata": {},
   "source": [
    "### The GAN models were trained using a conda environment. Below you can find how to create the same environment to train GAN models and generate sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd67f38-a1bf-4234-bbad-cf5baa718e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%bash\n",
    "# conda create --name deeplearning_py36_tf114_gpu python=3.6 tensorflow-gpu=1.14.0 keras-gpu=2.2.4\n",
    "# conda activate deeplearning_py36_tf114_gpu\n",
    "# conda install numpy=1.16.2  matplotlib=3.1.1 shap=0.29.3 ipykernel=5.1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03be807-48c6-42c9-90ec-f1a2e16d95f1",
   "metadata": {},
   "source": [
    "### Below you can find explanation of parameters that were used to train model and generate sequences.\n",
    "#### (These variables can be found in the beginning of __wgan_gp.py__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a272a4a-71da-40ac-9906-a74c62bfe3f8",
   "metadata": {},
   "source": [
    "__BATCH_SIZE__: Batch size (how many regions will be used in each iteration). \\\n",
    "__ITERS__: Number of batch iterations to train the model. \\\n",
    "__SEQ_LEN__: Length of the input sequences.  \\\n",
    "__SEQ_DIM__: Dimension of the input sequences. (4 nucleotides) \\\n",
    "__DIM__: Dimension of the model. It is used in latent space and convolutional layers.  \\\n",
    "__CRITIC_ITERS__: How many training iterations will be done for Discriminator for each Generator iteration.  \\\n",
    "__LAMBDA__: Hyperparameter for gradient penalty.  \\\n",
    "__loginterval__: Once every N iteration the log will be saved.  \\\n",
    "__seqinterval__: Once every N iteration the sample sequences will be generated.  \\\n",
    "__modelinterval__: Once every N iteration the model files will be saved.  \\\n",
    "__selectedmodel__: When generating sequences, the iteration number of the model you want to use. \\\n",
    "__suffix__: When generating sequences, the suffix to add to the header of the fasta regions. \\\n",
    "__ngenerate__: When generating sequences, number of sequences you want to generate relative to batchsize. Example: 1 (128 sequences will be generated if the batch size is 128) \\\n",
    "__outputdirc__: Path the to output folder. \\\n",
    "__fastafile__: Path to the fasta file to use as real enhancers "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a4ab1ed-8973-4026-9d66-98f2ca6d764d",
   "metadata": {},
   "source": [
    "### How to run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "832087a8-fa27-4250-bb4e-066a52ded090",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%bash\n",
    "# conda activate deeplearning_py36_tf114_gpu\n",
    "# python wgan_gp.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc65b44-515e-4dd8-9bb9-81d1f0ceaa13",
   "metadata": {},
   "source": [
    "### This will result following outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3d54fd-11d2-4fa8-92c7-b85c4007ad00",
   "metadata": {},
   "source": [
    "__./models/__: Folder containing saved model's weight files. \\\n",
    "__./samples_ACGT/__: Folder containing sampled sequences during training. \\\n",
    "__./samples_raw/__: Folder containing sampled sequences (in their raw format) during training.  \\\n",
    "__./gen_seq/__: Folder containing generated sequences after training. \\\n",
    "__./disc.json__: Architecture file of the discriminator.  \\\n",
    "__./gen.json__: Architecture file of the generator.  \\\n",
    "__./d_g_loss.pkl__: Logged loss values during training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aa94e0f-263e-4ad1-967e-059770526381",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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