HOML2 – Issue #439.ipynb

Issue #439.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": false
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      "id": "7fd7e1bb",
      "cell_type": "code",
      "source": "import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\n\nprint(\"TensorFlow:\", tf.__version__)\nprint(\"Keras:\", keras.__version__)\nprint(\"NumPy:\", np.__version__)",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "TensorFlow: 2.4.1\nKeras: 2.4.0\nNumPy: 1.19.5\n"
        }
      ]
    },
    {
      "metadata": {
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      "id": "8ae45046",
      "cell_type": "code",
      "source": "num_samples = 1000\nseq_length = 10\ninput_dims = 1\noutput_dims = 1\n\nX_train = tf.random.uniform((num_samples, seq_length, input_dims))\nX_valid = tf.random.uniform((num_samples, seq_length, input_dims))\ny_train = tf.random.uniform((num_samples, output_dims))\ny_valid = tf.random.uniform((num_samples, output_dims))",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "7154892a",
      "cell_type": "code",
      "source": "np.random.seed(42)\ntf.random.set_seed(42)\n\nmodel = keras.models.Sequential([\n    # this None is not for batch_size, the None for batch_size is still added behind the scene\n    # here [None, 1] means we want to make the input to be accepted with any length of step size as well\n    keras.layers.SimpleRNN(1, input_shape=[None, 1]) \n])\n\noptimizer = keras.optimizers.Adam(lr=0.005)\nmodel.compile(loss=\"mse\", optimizer=optimizer)\nhistory = model.fit(X_train, y_train, epochs=20,\n                    validation_data=(X_valid, y_valid))",
      "execution_count": 3,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Epoch 1/20\n32/32 [==============================] - 1s 21ms/step - loss: 0.1419 - val_loss: 0.1035\nEpoch 2/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0949 - val_loss: 0.0941\nEpoch 3/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0944 - val_loss: 0.0920\nEpoch 4/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0919 - val_loss: 0.0898\nEpoch 5/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0843 - val_loss: 0.0895\nEpoch 6/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0880 - val_loss: 0.0866\nEpoch 7/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0854 - val_loss: 0.0862\nEpoch 8/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0864 - val_loss: 0.0847\nEpoch 9/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0861 - val_loss: 0.0848\nEpoch 10/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0891 - val_loss: 0.0834\nEpoch 11/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0821 - val_loss: 0.0842\nEpoch 12/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0816 - val_loss: 0.0847\nEpoch 13/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0838 - val_loss: 0.0825\nEpoch 14/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0871 - val_loss: 0.0835\nEpoch 15/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0824\nEpoch 16/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0879 - val_loss: 0.0823\nEpoch 17/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0853 - val_loss: 0.0823\nEpoch 18/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0816 - val_loss: 0.0838\nEpoch 19/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0808 - val_loss: 0.0825\nEpoch 20/20\n32/32 [==============================] - 0s 3ms/step - loss: 0.0830 - val_loss: 0.0820\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
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      "id": "715f51d2",
      "cell_type": "code",
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      "execution_count": null,
      "outputs": []
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