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TensorFlow

TensorFlow

作者: 山天大畜 | 来源:发表于2017-04-18 15:49 被阅读84次

    Install

    $ sudo easy_install pip
    $ sudo pip install --upgrade virtualenv
    $ virtualenv --system-site-packages ~/tensorflow
    $ source ~/tensorflow/bin/activate
    
    //Choose 1
    $ pip install --upgrade tensorflow      # for Python 2.7
    $ pip3 install --upgrade tensorflow     # for Python 3.n
    $ pip install --upgrade tensorflow-gpu  # for Python 2.7 and GPU
    $ pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU
    #if failed(pip version lower than 8.1):
    $ pip install --upgrade TF_BINARY_URL   # Python 2.7
    $ pip3 install --upgrade TF_BINARY_URL  # Python 3.N
    #Find the appropriate value for *TF_BINARY_URL* for your system [here](https://www.tensorflow.org/install/install_mac#TF_BINARY_URL)
    
    

    Active

    $ source ~/tensorflow/bin/activate
    

    Unactive

    (tensorflow)$ deactivate
    

    Uninstall

    $ rm -r ~/tensorflow
    

    Run a short TensorFlow program

    $ python
    >>> import tensorflow as tf
    >>> hello = tf.constant('Hello, TensorFlow!')
    >>> sess = tf.Session()
    >>> print(sess.run(hello))
    

    Getting Started

    import tensorflow as tf
    node1 = tf.constant(3.0, tf.float32)
    node2 = tf.constant(4.0) # also tf.float32 implicitly
    print(node1, node2)
    #Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)
    
    sess = tf.Session()
    print(sess.run([node1, node2]))
    #[3.0, 4.0]
    
    node3 = tf.add(node1, node2)
    print("node3: ", node3)
    print("sess.run(node3): ",sess.run(node3))
    #node3:  Tensor("Add_2:0", shape=(), dtype=float32)
    #sess.run(node3):  7.0
    
    a = tf.placeholder(tf.float32)
    b = tf.placeholder(tf.float32)
    adder_node = a + b  # + provides a shortcut for tf.add(a, b)
    print(sess.run(adder_node, {a: 3, b:4.5}))
    print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
    #7.5
    #[ 3.  7.]
    add_and_triple = adder_node * 3.
    print(sess.run(add_and_triple, {a: 3, b:4.5}))
    #22.5
    

    Training

    W = tf.Variable([.3], tf.float32)
    b = tf.Variable([-.3], tf.float32)
    x = tf.placeholder(tf.float32)
    linear_model = W * x + b
    #Init
    init = tf.global_variables_initializer()
    sess.run(init)
    
    print(sess.run(linear_model, {x:[1,2,3,4]}))
    #[ 0.          0.30000001  0.60000002  0.90000004]
    

    Evaluate

    y = tf.placeholder(tf.float32)
    squared_deltas = tf.square(linear_model - y)
    loss = tf.reduce_sum(squared_deltas)
    print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
    #23.66
    fixW = tf.assign(W, [-1.])
    fixb = tf.assign(b, [1.])
    sess.run([fixW, fixb])
    print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
    #0.0
    

    Train

    optimizer = tf.train.GradientDescentOptimizer(0.01)
    train = optimizer.minimize(loss)
    sess.run(init) # reset values to incorrect defaults.
    for i in range(1000):
      sess.run(train, {x:[1,2,3,4], y:[0,-1,-2,-3]})
    
    print(sess.run([W, b]))
    #[array([-0.9999969], dtype=float32), array([ 0.99999082],
     dtype=float32)]
    

    理解梯度下降
    Completed trainable linear regression model
    回归与梯度下降

    import numpy as np
    import tensorflow as tf
    
    # Model parameters
    W = tf.Variable([.3], tf.float32)
    b = tf.Variable([-.3], tf.float32)
    # Model input and output
    x = tf.placeholder(tf.float32)
    linear_model = W * x + b
    y = tf.placeholder(tf.float32)
    # loss
    loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
    # optimizer
    optimizer = tf.train.GradientDescentOptimizer(0.01)
    train = optimizer.minimize(loss)
    # training data
    x_train = [1,2,3,4]
    y_train = [0,-1,-2,-3]
    # training loop
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init) # reset values to wrong
    for i in range(1000):
      sess.run(train, {x:x_train, y:y_train})
    
    # evaluate training accuracy
    curr_W, curr_b, curr_loss  = sess.run([W, b, loss], {x:x_train, y:y_train})
    print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
    
    #W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
    

    tf.contrib.learn

    import tensorflow as tf
    import numpy as np
    
    # Declare list of features. We only have one real-valued feature. There are many
    # other types of columns that are more complicated and useful.
    features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
    
    # An estimator is the front end to invoke training (fitting) and evaluation
    # (inference). There are many predefined types like linear regression,
    # logistic regression, linear classification, logistic classification, and
    # many neural network classifiers and regressors. The following code
    # provides an estimator that does linear regression.
    estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
    
    # TensorFlow provides many helper methods to read and set up data sets.
    # Here we use `numpy_input_fn`. We have to tell the function how many batches
    # of data (num_epochs) we want and how big each batch should be.
    x = np.array([1., 2., 3., 4.])
    y = np.array([0., -1., -2., -3.])
    input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4,
                                                  num_epochs=1000)
    
    # We can invoke 1000 training steps by invoking the `fit` method and passing the
    # training data set.
    estimator.fit(input_fn=input_fn, steps=1000)
    
    # Here we evaluate how well our model did. In a real example, we would want
    # to use a separate validation and testing data set to avoid overfitting.
    estimator.evaluate(input_fn=input_fn)
    
    #{'global_step': 1000, 'loss': 1.9650059e-11}
    

    custom model

    import numpy as np
    import tensorflow as tf
    # Declare list of features, we only have one real-valued feature
    def model(features, labels, mode):
      # Build a linear model and predict values
      W = tf.get_variable("W", [1], dtype=tf.float64)
      b = tf.get_variable("b", [1], dtype=tf.float64)
      y = W*features['x'] + b
      # Loss sub-graph
      loss = tf.reduce_sum(tf.square(y - labels))
      # Training sub-graph
      global_step = tf.train.get_global_step()
      optimizer = tf.train.GradientDescentOptimizer(0.01)
      train = tf.group(optimizer.minimize(loss),
                       tf.assign_add(global_step, 1))
      # ModelFnOps connects subgraphs we built to the
      # appropriate functionality.
      return tf.contrib.learn.ModelFnOps(
          mode=mode, predictions=y,
          loss=loss,
          train_op=train)
    
    estimator = tf.contrib.learn.Estimator(model_fn=model)
    # define our data set
    x = np.array([1., 2., 3., 4.])
    y = np.array([0., -1., -2., -3.])
    input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)
    
    # train
    estimator.fit(input_fn=input_fn, steps=1000)
    # evaluate our model
    print(estimator.evaluate(input_fn=input_fn, steps=10))
    

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