TensorFlow - Basic Minimisation with Gradient Descent

TensorFlow - Basic Minimisation with Gradient Descent.ipynb

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      "source": "import numpy as np\nimport tensorflow as tf",
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      "source": "### Using Gradient Descent, find the value of w  when\nW**2 - 10*W + 25 = 0"
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      "cell_type": "code",
      "source": "w = tf.Variable(0, dtype=tf.float32)",
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      "source": "cost = tf.add( tf.add(w**2, tf.multiply(-10.0, w )), 25)\ntrain = tf.train.GradientDescentOptimizer(0.01).minimize(cost)\ninit = tf.global_variables_initializer()\nsession = tf.Session()\nsession.run(init)\nprint(session.run(w))\n",
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      "source": "### Perform 1 round of Gradient Descent"
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      "source": "session.run(train)\nprint(session.run(w))",
      "execution_count": 47,
      "outputs": [
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          "text": "0.099999994\n",
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      "cell_type": "markdown",
      "source": "### Perform 1000 round of Gradient Descent"
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      "source": "for i in range(1_000):\n    session.run(train)\nprint(session.run(w))",
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      "source": "### Test"
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      "source": "5**2 - 10*5 +25",
      "execution_count": 53,
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      "cell_type": "markdown",
      "source": "### Overload Version"
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      "cell_type": "code",
      "source": "x = tf.Variable(0, dtype=tf.float32)\ncost = w**2 - 10*w + 25\ntrain = tf.train.GradientDescentOptimizer(0.01).minimize(cost)\ninit = tf.global_variables_initializer()\nsession = tf.Session()\nsession.run(init)\nprint(session.run(w))",
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      "cell_type": "code",
      "source": "for i in range(1_000):\n    session.run(train)\nprint(session.run(w))",
      "execution_count": 57,
      "outputs": [
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          "text": "4.9999886\n",
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