mirror of
https://github.com/vale981/ray
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3ebfd850e1
* Test examples for pep8 compliance. * Make rl_pong example pep8 compliant. * Make policy gradient example pep8 compliant. * Make lbfgs example pep8 compliant. * Make hyperopt example pep8 compliant. * Make a3c example pep8 compliant. * Make evolution strategies example pep8 compliant. * Make resnet example pep8 compliant. * Fix.
113 lines
4.5 KiB
Python
113 lines
4.5 KiB
Python
# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
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# CNNs to train MNIST
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# https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html#build-a-multilayer-convolutional-network. # noqa: E501
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import ray
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import tensorflow as tf
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def get_batch(data, batch_index, batch_size):
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# This method currently drops data when num_data is not divisible by
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# batch_size.
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num_data = data.shape[0]
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num_batches = num_data // batch_size
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batch_index %= num_batches
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return data[(batch_index * batch_size):((batch_index + 1) * batch_size)]
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def weight(shape, stddev):
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initial = tf.truncated_normal(shape, stddev=stddev)
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return tf.Variable(initial)
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def bias(shape):
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initial = tf.constant(0.1, shape=shape)
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return tf.Variable(initial)
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def conv2d(x, W):
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return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
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def max_pool_2x2(x):
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return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
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padding="SAME")
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def cnn_setup(x, y, keep_prob, lr, stddev):
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first_hidden = 32
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second_hidden = 64
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fc_hidden = 1024
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W_conv1 = weight([5, 5, 1, first_hidden], stddev)
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B_conv1 = bias([first_hidden])
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x_image = tf.reshape(x, [-1, 28, 28, 1])
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h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + B_conv1)
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h_pool1 = max_pool_2x2(h_conv1)
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W_conv2 = weight([5, 5, first_hidden, second_hidden], stddev)
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b_conv2 = bias([second_hidden])
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h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
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h_pool2 = max_pool_2x2(h_conv2)
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W_fc1 = weight([7 * 7 * second_hidden, fc_hidden], stddev)
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b_fc1 = bias([fc_hidden])
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h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * second_hidden])
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h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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W_fc2 = weight([fc_hidden, 10], stddev)
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b_fc2 = bias([10])
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y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
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cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_conv),
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reduction_indices=[1]))
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correct_pred = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
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return (tf.train.AdamOptimizer(lr).minimize(cross_entropy),
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tf.reduce_mean(tf.cast(correct_pred, tf.float32)), cross_entropy)
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# Define a remote function that takes a set of hyperparameters as well as the
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# data, consructs and trains a network, and returns the validation accuracy.
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@ray.remote
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def train_cnn_and_compute_accuracy(params, steps, train_images, train_labels,
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validation_images, validation_labels,
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weights=None):
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# Extract the hyperparameters from the params dictionary.
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learning_rate = params["learning_rate"]
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batch_size = params["batch_size"]
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keep = 1 - params["dropout"]
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stddev = params["stddev"]
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# Create the network and related variables.
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with tf.Graph().as_default():
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# Create the input placeholders for the network.
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x = tf.placeholder(tf.float32, shape=[None, 784])
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y = tf.placeholder(tf.float32, shape=[None, 10])
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keep_prob = tf.placeholder(tf.float32)
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# Create the network.
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train_step, accuracy, loss = cnn_setup(x, y, keep_prob, learning_rate,
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stddev)
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# Do the training and evaluation.
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with tf.Session() as sess:
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# Use the TensorFlowVariables utility. This is only necessary if we want
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# to set and get the weights.
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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# Initialize the network weights.
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sess.run(tf.global_variables_initializer())
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# If some network weights were passed in, set those.
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if weights is not None:
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variables.set_weights(weights)
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# Do some steps of training.
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for i in range(1, steps + 1):
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# Fetch the next batch of data.
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image_batch = get_batch(train_images, i, batch_size)
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label_batch = get_batch(train_labels, i, batch_size)
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# Do one step of training.
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sess.run(train_step, feed_dict={x: image_batch, y: label_batch,
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keep_prob: keep})
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# Training is done, so compute the validation accuracy and the current
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# weights and return.
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totalacc = accuracy.eval(feed_dict={x: validation_images,
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y: validation_labels,
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keep_prob: 1.0})
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new_weights = variables.get_weights()
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return float(totalacc), new_weights
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