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        "author": "Cristian Garcia",
        "author_email": "cgarcia.e88@gmail.com",
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        "description": "# Tensor Builder\nTensorBuilder is a TensorFlow-based library that enables you to easily create complex neural networks using functional programming.\n\n##Import\n\nFor demonstration purposes we will import right now everything we will need for the rest of the exercises like this\n```python\nfrom tensorbuilder.api import *\nimport tensorflow as tf\n```\nbut you can also import just what you need from the `tensorbuilder` module.\n\n## Phi\n#### Lambdas\nWith the `T` object you can create quick math-like lambdas using any operator, this lets you write things like\n```python\nx, b = tf.placeholder('float'), tf.placeholder('float')\n\nf = (T + b) / (T + 10)  #lambda x: (x + b) / (x + 10)\ny = f(x)\n\nassert \"div\" in y.name\n```\n\n#### Composition\nUse function composition with the `>>` operator to improve readability\n```python\nx, w, b = tf.placeholder('float', [None, 5]),  tf.placeholder('float', [5, 3]), tf.placeholder('float', [3])\n\nf = T.matmul(w) >> T + b >> T.sigmoid()\ny = f(x)\n\nassert \"Sigmoid\" in y.name\n```\n\n## tf + nn\nAny function from the `tf` and `nn` modules is a method from the `T` object, as before you can use the `>>` operator or you can chain them to produce complex functions\n```python\nx, w, b = tf.placeholder('float', [None, 5]),  tf.placeholder('float', [5, 3]), tf.placeholder('float', [3])\n\nf = T.matmul(w).add(b).sigmoid()\ny = f(x)\n\nassert \"Sigmoid\" in y.name\n```\n## layers\n#### affine\nYou can use functions from the `tf.contrib.layers` module via the `T.layers` property. Here we will use [Pipe](https://github.com/cgarciae/phi#seq-and-pipe) to apply a value directly to an expression:\n```python\nx = tf.placeholder('float', [None, 5])\n\ny = Pipe(\n  x,\n  T.layers.fully_connected(64, activation_fn=tf.nn.sigmoid)  # sigmoid layer 64\n  .layers.fully_connected(32, activation_fn=tf.nn.tanh)  # tanh layer 32\n  .layers.fully_connected(16, activation_fn=None)  # linear layer 16\n  .layers.fully_connected(8, activation_fn=tf.nn.relu)  # relu layer 8\n)\n\nassert \"Relu\" in y.name\n```\nHowever, since it is such a common task to build fully_connected layers using the different functions from the `tf.nn` module, we've (dynamically) create all combination of these as their own methods so you con rewrite the previous as\n```python\nx = tf.placeholder('float', [None, 5])\n\ny = Pipe(\n  x,\n  T.sigmoid_layer(64)  # sigmoid layer 64\n  .tanh_layer(32)  # tanh layer 32\n  .linear_layer(16)  # linear layer 16\n  .relu_layer(8)  # relu layer 8\n)\n\nassert \"Relu\" in y.name\n```\nThe latter is much more compact, English readable, and reduces a lot of noise.\n\n#### convolutional\nComing soon!\n\n## leveraging phi\nComing soon!\n\n## summary\nComing soon!\n\n## other ops\nComing soon!\n\n## Installation\nTensor Builder assumes you have a working `tensorflow` installation. We don't include it in the `requirements.txt` since the installation of tensorflow varies depending on your setup.\n\n#### From pypi\n```\npip install tensorbuilder\n```\n\n#### From github\nFor the latest development version\n```\npip install git+https://github.com/cgarciae/tensorbuilder.git@develop\n```\n\n## Getting Started\n\nCreate neural network with a [5, 10, 3] architecture with a `softmax` output layer and a `tanh` hidden layer through a Builder and then get back its tensor:\n\n```python\nimport tensorflow as tf\nfrom tensorbuilder import T\n\nx = tf.placeholder(tf.float32, shape=[None, 5])\nkeep_prob = tf.placeholder(tf.float32)\n\nh = T.Pipe(\n  x,\n  T.tanh_layer(10) # tanh(x * w + b)\n  .dropout(keep_prob) # dropout(x, keep_prob)\n  .softmax_layer(3) # softmax(x * w + b)\n)\n```\n\n## Features\nComming Soon!\n\n## Documentation\nComming Soon!\n\n## The Guide\nComming Soon!\n\n## Full Example\nNext is an example with all the features of TensorBuilder including the DSL, branching and scoping. It creates a branched computation where each branch is executed on a different device. All branches are then reduced to a single layer, but the computation is the branched again to obtain both the activation function and the trainer.\n\n```python\nimport tensorflow as tf\nfrom tensorbuilder import T\n\nx = placeholder(tf.float32, shape=[None, 10])\ny = placeholder(tf.float32, shape=[None, 5])\n\n[activation, trainer] = T.Pipe(\n    x,\n    [\n        T.With( tf.device(\"/gpu:0\"):\n            T.relu_layer(20)\n        )\n    ,\n        T.With( tf.device(\"/gpu:1\"):\n            T.sigmoid_layer(20)\n        )\n    ,\n        T.With( tf.device(\"/cpu:0\"):\n            T.tanh_layer(20)\n        )\n    ],\n    T.linear_layer(5),\n    [\n        T.softmax() # activation\n    ,\n        T\n        .softmax_cross_entropy_with_logits(y) # loss\n        .minimize(tf.train.AdamOptimizer(0.01)) # trainer\n    ]\n)\n```",
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        "keywords": "tensorflow,deep learning,neural networks",
        "license": "MIT",
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