scrnn_lowsnr.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "scrnn_lowsnr.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "COXiFhxo_S87",
        "colab_type": "code",
        "outputId": "b3c6b5b6-14b8-42dc-af8e-d13c24aac104",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras.layers import Input,Dense,Flatten,MaxPool1D,Activation,LeakyReLU,LSTM,BatchNormalization,Dropout, Conv1D,Lambda\n",
        "from keras.models import Model,load_model\n",
        "import pandas as pd\n",
        "import keras.backend as K\n",
        "import h5py\n",
        "import model_architectures as ma\n",
        "import utils"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SwyW6KaOBhwq",
        "colab_type": "code",
        "outputId": "8bda1771-b701-4161-86f3-091b39f0e5ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z9PhjQA_S8-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "models_dir = '/content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/'\n",
        "feat_file='/content/gdrive/My Drive/Project-EE5611/project_features/base_scrnn_lowsnr_features.h5'\n",
        "fid=h5py.File(feat_file,'r');\n",
        "x_tr=fid['x_tr']\n",
        "y_tr=fid['y_tr'] \n",
        "snr_tr = np.array(fid['snr_tr'])\n",
        "x_ts=fid['x_ts']\n",
        "y_ts=fid['y_ts']\n",
        "snr_ts = np.array(fid['snr_ts'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lrj_O39EKzrn",
        "colab_type": "code",
        "outputId": "7c38a0ef-9678-41cc-bccf-cdb40f2d3f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "m1 = ma.base_scrnn()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 128, 2)            0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 128, 2)            8         \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 124, 128)          1408      \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 41, 128)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 37, 128)           82048     \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 37, 128)           131584    \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 37, 128)           131584    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 37, 128)           0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 4736)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 11)                52107     \n",
            "=================================================================\n",
            "Total params: 398,739\n",
            "Trainable params: 398,735\n",
            "Non-trainable params: 4\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNDUSZVLLvmA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "checkpoint = keras.callbacks.ModelCheckpoint(models_dir+\"model_epoch-{epoch:02d}_val_acc-{val_accuracy:.2f}.h5\", monitor='val_accuracy', verbose=2, save_best_only=False, save_weights_only=False, mode='auto', period=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQdYici6MzxW",
        "colab_type": "code",
        "outputId": "50c040a2-0d8d-446d-e9c4-f5d05645ad8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = m1.fit(x=x_tr,\n",
        "       y=y_tr,\n",
        "       validation_data=[x_ts,y_ts],\n",
        "       batch_size=128,\n",
        "       epochs=75,\n",
        "       shuffle='batch',\n",
        "       callbacks = [checkpoint],\n",
        "       verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 108900 samples, validate on 22000 samples\n",
            "Epoch 1/75\n",
            "108900/108900 [==============================] - 70s 646us/step - loss: 2.0490 - accuracy: 0.2312 - val_loss: 1.7338 - val_accuracy: 0.3564\n",
            "\n",
            "Epoch 00001: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-01_val_acc-0.36.h5\n",
            "Epoch 2/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 1.8432 - accuracy: 0.2991 - val_loss: 2.1664 - val_accuracy: 0.3713\n",
            "\n",
            "Epoch 00002: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-02_val_acc-0.37.h5\n",
            "Epoch 3/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.7994 - accuracy: 0.3176 - val_loss: 2.2606 - val_accuracy: 0.3570\n",
            "\n",
            "Epoch 00003: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-03_val_acc-0.36.h5\n",
            "Epoch 4/75\n",
            "108900/108900 [==============================] - 59s 537us/step - loss: 1.7722 - accuracy: 0.3295 - val_loss: 2.2952 - val_accuracy: 0.3675\n",
            "\n",
            "Epoch 00004: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-04_val_acc-0.37.h5\n",
            "Epoch 5/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 1.7639 - accuracy: 0.3324 - val_loss: 2.0419 - val_accuracy: 0.3989\n",
            "\n",
            "Epoch 00005: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-05_val_acc-0.40.h5\n",
            "Epoch 6/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.7468 - accuracy: 0.3380 - val_loss: 2.4089 - val_accuracy: 0.3714\n",
            "\n",
            "Epoch 00006: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-06_val_acc-0.37.h5\n",
            "Epoch 7/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.7396 - accuracy: 0.3413 - val_loss: 1.8743 - val_accuracy: 0.4247\n",
            "\n",
            "Epoch 00007: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-07_val_acc-0.42.h5\n",
            "Epoch 8/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.7273 - accuracy: 0.3468 - val_loss: 3.6898 - val_accuracy: 0.3179\n",
            "\n",
            "Epoch 00008: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-08_val_acc-0.32.h5\n",
            "Epoch 9/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 1.7177 - accuracy: 0.3508 - val_loss: 2.0691 - val_accuracy: 0.3949\n",
            "\n",
            "Epoch 00009: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-09_val_acc-0.39.h5\n",
            "Epoch 10/75\n",
            "108900/108900 [==============================] - 59s 537us/step - loss: 1.7108 - accuracy: 0.3528 - val_loss: 2.3480 - val_accuracy: 0.3817\n",
            "\n",
            "Epoch 00010: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-10_val_acc-0.38.h5\n",
            "Epoch 11/75\n",
            "108900/108900 [==============================] - 59s 537us/step - loss: 1.7003 - accuracy: 0.3587 - val_loss: 2.7046 - val_accuracy: 0.3539\n",
            "\n",
            "Epoch 00011: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-11_val_acc-0.35.h5\n",
            "Epoch 12/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.6934 - accuracy: 0.3618 - val_loss: 2.8755 - val_accuracy: 0.3617\n",
            "\n",
            "Epoch 00012: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-12_val_acc-0.36.h5\n",
            "Epoch 13/75\n",
            "108900/108900 [==============================] - 59s 537us/step - loss: 1.6881 - accuracy: 0.3625 - val_loss: 2.5219 - val_accuracy: 0.3541\n",
            "\n",
            "Epoch 00013: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-13_val_acc-0.35.h5\n",
            "Epoch 14/75\n",
            "108900/108900 [==============================] - 59s 541us/step - loss: 1.6954 - accuracy: 0.3598 - val_loss: 1.2922 - val_accuracy: 0.5164\n",
            "\n",
            "Epoch 00014: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-14_val_acc-0.52.h5\n",
            "Epoch 15/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.6793 - accuracy: 0.3663 - val_loss: 1.7637 - val_accuracy: 0.4419\n",
            "\n",
            "Epoch 00015: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-15_val_acc-0.44.h5\n",
            "Epoch 16/75\n",
            "108900/108900 [==============================] - 59s 538us/step - loss: 1.6689 - accuracy: 0.3705 - val_loss: 1.8416 - val_accuracy: 0.4371\n",
            "\n",
            "Epoch 00016: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-16_val_acc-0.44.h5\n",
            "Epoch 17/75\n",
            "108900/108900 [==============================] - 58s 532us/step - loss: 1.6655 - accuracy: 0.3709 - val_loss: 2.0251 - val_accuracy: 0.4222\n",
            "\n",
            "Epoch 00017: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-17_val_acc-0.42.h5\n",
            "Epoch 18/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.6575 - accuracy: 0.3754 - val_loss: 4.0461 - val_accuracy: 0.3673\n",
            "\n",
            "Epoch 00018: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-18_val_acc-0.37.h5\n",
            "Epoch 19/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.6496 - accuracy: 0.3785 - val_loss: 8.4533 - val_accuracy: 0.3371\n",
            "\n",
            "Epoch 00019: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-19_val_acc-0.34.h5\n",
            "Epoch 20/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.6452 - accuracy: 0.3802 - val_loss: 2.9100 - val_accuracy: 0.3715\n",
            "\n",
            "Epoch 00020: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-20_val_acc-0.37.h5\n",
            "Epoch 21/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.6374 - accuracy: 0.3842 - val_loss: 4.2376 - val_accuracy: 0.3429\n",
            "\n",
            "Epoch 00021: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-21_val_acc-0.34.h5\n",
            "Epoch 22/75\n",
            "108900/108900 [==============================] - 58s 534us/step - loss: 1.6411 - accuracy: 0.3837 - val_loss: 3.3598 - val_accuracy: 0.3365\n",
            "\n",
            "Epoch 00022: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-22_val_acc-0.34.h5\n",
            "Epoch 23/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.6253 - accuracy: 0.3871 - val_loss: 2.5395 - val_accuracy: 0.3863\n",
            "\n",
            "Epoch 00023: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-23_val_acc-0.39.h5\n",
            "Epoch 24/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.6191 - accuracy: 0.3898 - val_loss: 2.8015 - val_accuracy: 0.3802\n",
            "\n",
            "Epoch 00024: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-24_val_acc-0.38.h5\n",
            "Epoch 25/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 1.6134 - accuracy: 0.3910 - val_loss: 4.9881 - val_accuracy: 0.3200\n",
            "\n",
            "Epoch 00025: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-25_val_acc-0.32.h5\n",
            "Epoch 26/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 1.6068 - accuracy: 0.3947 - val_loss: 3.4732 - val_accuracy: 0.3382\n",
            "\n",
            "Epoch 00026: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-26_val_acc-0.34.h5\n",
            "Epoch 27/75\n",
            "108900/108900 [==============================] - 58s 534us/step - loss: 1.6007 - accuracy: 0.3971 - val_loss: 6.4049 - val_accuracy: 0.3176\n",
            "\n",
            "Epoch 00027: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-27_val_acc-0.32.h5\n",
            "Epoch 28/75\n",
            "108900/108900 [==============================] - 59s 537us/step - loss: 1.5944 - accuracy: 0.4004 - val_loss: 3.7306 - val_accuracy: 0.3539\n",
            "\n",
            "Epoch 00028: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-28_val_acc-0.35.h5\n",
            "Epoch 29/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 1.5857 - accuracy: 0.4027 - val_loss: 3.4347 - val_accuracy: 0.3660\n",
            "\n",
            "Epoch 00029: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-29_val_acc-0.37.h5\n",
            "Epoch 30/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.5803 - accuracy: 0.4059 - val_loss: 1.9859 - val_accuracy: 0.3984\n",
            "\n",
            "Epoch 00030: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-30_val_acc-0.40.h5\n",
            "Epoch 31/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.5770 - accuracy: 0.4071 - val_loss: 5.1354 - val_accuracy: 0.3330\n",
            "\n",
            "Epoch 00031: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-31_val_acc-0.33.h5\n",
            "Epoch 32/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 1.5640 - accuracy: 0.4135 - val_loss: 3.4015 - val_accuracy: 0.3815\n",
            "\n",
            "Epoch 00032: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-32_val_acc-0.38.h5\n",
            "Epoch 33/75\n",
            "108900/108900 [==============================] - 59s 538us/step - loss: 1.5571 - accuracy: 0.4146 - val_loss: 3.3327 - val_accuracy: 0.3665\n",
            "\n",
            "Epoch 00033: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-33_val_acc-0.37.h5\n",
            "Epoch 34/75\n",
            "108900/108900 [==============================] - 59s 542us/step - loss: 1.5492 - accuracy: 0.4173 - val_loss: 3.9024 - val_accuracy: 0.3591\n",
            "\n",
            "Epoch 00034: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-34_val_acc-0.36.h5\n",
            "Epoch 35/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 1.5454 - accuracy: 0.4199 - val_loss: 4.1326 - val_accuracy: 0.3339\n",
            "\n",
            "Epoch 00035: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-35_val_acc-0.33.h5\n",
            "Epoch 36/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 1.5385 - accuracy: 0.4228 - val_loss: 5.0808 - val_accuracy: 0.3335\n",
            "\n",
            "Epoch 00036: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-36_val_acc-0.33.h5\n",
            "Epoch 37/75\n",
            "108900/108900 [==============================] - 59s 541us/step - loss: 1.5296 - accuracy: 0.4262 - val_loss: 3.0892 - val_accuracy: 0.3684\n",
            "\n",
            "Epoch 00037: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-37_val_acc-0.37.h5\n",
            "Epoch 38/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 2.1639 - accuracy: 0.2814 - val_loss: 2.0549 - val_accuracy: 0.2866\n",
            "\n",
            "Epoch 00038: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-38_val_acc-0.29.h5\n",
            "Epoch 39/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 2.0885 - accuracy: 0.2355 - val_loss: 1.9980 - val_accuracy: 0.3295\n",
            "\n",
            "Epoch 00039: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-39_val_acc-0.33.h5\n",
            "Epoch 40/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 3.4650 - accuracy: 0.2090 - val_loss: 2.8624 - val_accuracy: 0.1994\n",
            "\n",
            "Epoch 00040: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-40_val_acc-0.20.h5\n",
            "Epoch 41/75\n",
            "108900/108900 [==============================] - 59s 542us/step - loss: 2.5965 - accuracy: 0.1678 - val_loss: 2.4011 - val_accuracy: 0.2218\n",
            "\n",
            "Epoch 00041: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-41_val_acc-0.22.h5\n",
            "Epoch 42/75\n",
            "108900/108900 [==============================] - 59s 540us/step - loss: 10.7241 - accuracy: 0.1586 - val_loss: 2.5766 - val_accuracy: 0.2176\n",
            "\n",
            "Epoch 00042: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-42_val_acc-0.22.h5\n",
            "Epoch 43/75\n",
            "108900/108900 [==============================] - 59s 540us/step - loss: 6.5545 - accuracy: 0.1642 - val_loss: 5.9606 - val_accuracy: 0.1713\n",
            "\n",
            "Epoch 00043: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-43_val_acc-0.17.h5\n",
            "Epoch 44/75\n",
            "108900/108900 [==============================] - 59s 541us/step - loss: 3.0896 - accuracy: 0.1644 - val_loss: 4.8756 - val_accuracy: 0.1992\n",
            "\n",
            "Epoch 00044: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-44_val_acc-0.20.h5\n",
            "Epoch 45/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 2.9383 - accuracy: 0.1488 - val_loss: 2.2045 - val_accuracy: 0.2171\n",
            "\n",
            "Epoch 00045: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-45_val_acc-0.22.h5\n",
            "Epoch 46/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 2.2651 - accuracy: 0.1752 - val_loss: 2.1245 - val_accuracy: 0.2277\n",
            "\n",
            "Epoch 00046: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-46_val_acc-0.23.h5\n",
            "Epoch 47/75\n",
            "108900/108900 [==============================] - 59s 541us/step - loss: 2.2326 - accuracy: 0.1851 - val_loss: 2.2437 - val_accuracy: 0.2325\n",
            "\n",
            "Epoch 00047: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-47_val_acc-0.23.h5\n",
            "Epoch 48/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 2.2027 - accuracy: 0.1931 - val_loss: 2.1101 - val_accuracy: 0.2469\n",
            "\n",
            "Epoch 00048: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-48_val_acc-0.25.h5\n",
            "Epoch 49/75\n",
            "108900/108900 [==============================] - 58s 534us/step - loss: 2.1876 - accuracy: 0.1990 - val_loss: 2.0729 - val_accuracy: 0.2543\n",
            "\n",
            "Epoch 00049: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-49_val_acc-0.25.h5\n",
            "Epoch 50/75\n",
            "108900/108900 [==============================] - 58s 534us/step - loss: 2.1738 - accuracy: 0.2024 - val_loss: 2.0742 - val_accuracy: 0.2622\n",
            "\n",
            "Epoch 00050: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-50_val_acc-0.26.h5\n",
            "Epoch 51/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 2.1633 - accuracy: 0.2058 - val_loss: 2.1342 - val_accuracy: 0.2609\n",
            "\n",
            "Epoch 00051: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-51_val_acc-0.26.h5\n",
            "Epoch 52/75\n",
            "108900/108900 [==============================] - 59s 537us/step - loss: 2.1537 - accuracy: 0.2082 - val_loss: 2.1324 - val_accuracy: 0.2663\n",
            "\n",
            "Epoch 00052: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-52_val_acc-0.27.h5\n",
            "Epoch 53/75\n",
            "108900/108900 [==============================] - 59s 539us/step - loss: 2.1470 - accuracy: 0.2116 - val_loss: 2.1246 - val_accuracy: 0.2704\n",
            "\n",
            "Epoch 00053: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-53_val_acc-0.27.h5\n",
            "Epoch 54/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 2.1343 - accuracy: 0.2141 - val_loss: 2.0656 - val_accuracy: 0.2731\n",
            "\n",
            "Epoch 00054: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-54_val_acc-0.27.h5\n",
            "Epoch 55/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 2.1343 - accuracy: 0.2147 - val_loss: 2.1031 - val_accuracy: 0.2751\n",
            "\n",
            "Epoch 00055: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-55_val_acc-0.28.h5\n",
            "Epoch 56/75\n",
            "108900/108900 [==============================] - 59s 541us/step - loss: 2.1271 - accuracy: 0.2185 - val_loss: 2.0520 - val_accuracy: 0.2777\n",
            "\n",
            "Epoch 00056: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-56_val_acc-0.28.h5\n",
            "Epoch 57/75\n",
            "108900/108900 [==============================] - 58s 537us/step - loss: 2.1061 - accuracy: 0.2239 - val_loss: 2.0217 - val_accuracy: 0.2870\n",
            "\n",
            "Epoch 00057: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-57_val_acc-0.29.h5\n",
            "Epoch 58/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 2.1149 - accuracy: 0.2224 - val_loss: 2.4837 - val_accuracy: 0.2647\n",
            "\n",
            "Epoch 00058: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-58_val_acc-0.26.h5\n",
            "Epoch 59/75\n",
            "108900/108900 [==============================] - 58s 535us/step - loss: 2.2183 - accuracy: 0.2056 - val_loss: 5.2011 - val_accuracy: 0.1752\n",
            "\n",
            "Epoch 00059: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-59_val_acc-0.18.h5\n",
            "Epoch 60/75\n",
            "108900/108900 [==============================] - 59s 538us/step - loss: 3.2870 - accuracy: 0.1655 - val_loss: 2.2516 - val_accuracy: 0.2202\n",
            "\n",
            "Epoch 00060: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-60_val_acc-0.22.h5\n",
            "Epoch 61/75\n",
            "108900/108900 [==============================] - 58s 533us/step - loss: 2.2731 - accuracy: 0.1734 - val_loss: 2.3267 - val_accuracy: 0.2051\n",
            "\n",
            "Epoch 00061: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-61_val_acc-0.21.h5\n",
            "Epoch 62/75\n",
            "108900/108900 [==============================] - 59s 538us/step - loss: 2.2381 - accuracy: 0.1780 - val_loss: 2.3329 - val_accuracy: 0.2016\n",
            "\n",
            "Epoch 00062: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-62_val_acc-0.20.h5\n",
            "Epoch 63/75\n",
            "108900/108900 [==============================] - 59s 542us/step - loss: 2.2242 - accuracy: 0.1843 - val_loss: 2.2522 - val_accuracy: 0.2209\n",
            "\n",
            "Epoch 00063: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-63_val_acc-0.22.h5\n",
            "Epoch 64/75\n",
            "108900/108900 [==============================] - 58s 536us/step - loss: 2.2169 - accuracy: 0.1873 - val_loss: 2.3543 - val_accuracy: 0.2088\n",
            "\n",
            "Epoch 00064: saving model to /content/gdrive/My Drive/Project-EE5611/Model_files/models/scrnn_lowsnr/model_epoch-64_val_acc-0.21.h5\n",
            "Epoch 65/75\n",
            "102656/108900 [===========================>..] - ETA: 3s - loss: 2.2091 - accuracy: 0.1886"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-59cdd2773d84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m        \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'batch'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m        \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m        verbose=1)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3790\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3791\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3792\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3794\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m     \"\"\"\n\u001b[0;32m-> 1605\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1643\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1644\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1645\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}