KerasTest.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import numpy.random as npr\n",
    "import matplotlib.pyplot\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "model = keras.Sequential([\n",
    "    keras.Input(shape=(5,)),\n",
    "    layers.Dense(4, activation=\"tanh\"),\n",
    "    layers.Dense(2, activation=\"tanh\")\n",
    "])"
   ]
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   "cell_type": "code",
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   "source": [
    "print(model.summary())"
   ]
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   "source": [
    "X = npr.rand(5).reshape(1,-1)\n",
    "y = np.array([0.5,0.4]).reshape(1,2)\n",
    "print(X)\n",
    "print(y)\n",
    "# \n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# %load_ext tensorboard\n",
    "\n",
    "model.compile(optimizer=keras.optimizers.SGD(learning_rate=1),\n",
    "#               optimizer=tf.compat.v1.train.GradientDescentOptimizer,\n",
    "             loss=keras.losses.MeanSquaredError())\n",
    "\n",
    "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "model.fit(X, y, epochs=1500, callbacks=[tensorboard_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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