python类global_variables_initializer()的实例源码

run.py 文件源码 项目:handwritten-sequence-tensorflow 作者: johnsmithm 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def fast_run(args):
    model = Model(args)
    feed = {}
    #feed[model.train_batch]=False
    xx,ss,yy=model.inputs(args.input_path)

    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)
    tf.train.start_queue_runners(sess=sess)
    xxx,sss,yyy=sess.run([xx,ss,yy])
    #print(yyy)
    #print(yyy[1])
    print('len:',xxx.shape)
    import matplotlib.cm as cm
    import matplotlib as mpl
    mpl.use('Agg')
    import matplotlib.pyplot as plt
    plt.figure(figsize=(16,4))
    #plt.imshow()
    plt.imshow(np.asarray(xxx[0]).reshape((36,90))+0.5, interpolation='nearest', aspect='auto', cmap=cm.jet)
    plt.savefig("img.jpg")
    plt.clf() ; plt.cla()
export_model.py 文件源码 项目:yt8m 作者: forwchen 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def export_model(self, model_dir, global_step_val, last_checkpoint):
    """Exports the model so that it can used for batch predictions."""

    with self.graph.as_default():
      with tf.Session() as session:
        session.run(tf.global_variables_initializer())
        self.saver.restore(session, last_checkpoint)

        signature = signature_def_utils.build_signature_def(
            inputs=self.inputs,
            outputs=self.outputs,
            method_name=signature_constants.PREDICT_METHOD_NAME)

        signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                         signature}

        model_builder = saved_model_builder.SavedModelBuilder(model_dir)
        model_builder.add_meta_graph_and_variables(session,
            tags=[tag_constants.SERVING],
            signature_def_map=signature_map,
            clear_devices=True)
        model_builder.save()
unittests.py 文件源码 项目:DNC 作者: bgavran 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def test_link_matrix(self):
        b, n = 2, 5
        write_weighting = np.random.rand(b, n)
        precedence_weighting = np.random.rand(b, n)  # precedence weighting from previous time step
        link_matrix_old = np.random.rand(b, n, n) * (
            1 - np.tile(np.eye(5), [b, 1, 1]))  # random link matrix with diagonals zero
        link_matrix_correct = np.zeros((b, n, n))
        for k in range(b):
            for i in range(n):
                for j in range(n):
                    if i != j:
                        link_matrix_correct[k, i, j] = (1 - write_weighting[k, i] - write_weighting[k, j]) * \
                                                       link_matrix_old[k, i, j] + \
                                                       write_weighting[k, i] * precedence_weighting[k, j]

        with self.test_session():
            tf.global_variables_initializer().run()
            Memory.batch_size = b
            Memory.memory_size = n
            new_link_matrix = Memory.update_link_matrix(Memory,
                                                        tf.constant(link_matrix_old, dtype=tf.float32),
                                                        tf.constant(precedence_weighting, dtype=tf.float32),
                                                        tf.constant(write_weighting, dtype=tf.float32))
            self.assertAllClose(link_matrix_correct, new_link_matrix.eval())
test_attacks.py 文件源码 项目:cleverhans 作者: tensorflow 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def setUp(self):
        super(TestVirtualAdversarialMethod, self).setUp()
        import tensorflow as tf
        import tensorflow.contrib.slim as slim

        def dummy_model(x):
            net = slim.fully_connected(x, 60)
            return slim.fully_connected(net, 10, activation_fn=None)

        self.sess = tf.Session()
        self.sess.as_default()
        self.model = tf.make_template('dummy_model', dummy_model)
        self.attack = VirtualAdversarialMethod(self.model, sess=self.sess)

        # initialize model
        with tf.name_scope('dummy_model'):
            self.model(tf.placeholder(tf.float32, shape=(None, 1000)))
        self.sess.run(tf.global_variables_initializer())
test_attacks.py 文件源码 项目:cleverhans 作者: tensorflow 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def setUp(self):
        super(TestSaliencyMapMethod, self).setUp()
        import tensorflow as tf
        import tensorflow.contrib.slim as slim

        def dummy_model(x):
            net = slim.fully_connected(x, 60)
            return slim.fully_connected(net, 10, activation_fn=None)

        self.sess = tf.Session()
        self.sess.as_default()
        self.model = tf.make_template('dummy_model', dummy_model)
        self.attack = SaliencyMapMethod(self.model, sess=self.sess)

        # initialize model
        with tf.name_scope('dummy_model'):
            self.model(tf.placeholder(tf.float32, shape=(None, 1000)))
        self.sess.run(tf.global_variables_initializer())

        self.attack = SaliencyMapMethod(self.model, sess=self.sess)
inception_v2_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v2(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
inception_v2_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v2(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
inception_v2_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 41 收藏 0 点赞 0 评论 0
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v2(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
inception_resnet_v2_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000
    with self.test_session() as sess:
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      inception.inception_resnet_v2(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = inception.inception_resnet_v2(eval_inputs,
                                                num_classes,
                                                is_training=False,
                                                reuse=True)
      predictions = tf.argmax(logits, 1)
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
resnet_v2_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testUnknownBatchSize(self):
    batch = 2
    height, width = 65, 65
    global_pool = True
    num_classes = 10
    inputs = create_test_input(None, height, width, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      logits, _ = self._resnet_small(inputs, num_classes,
                                     global_pool=global_pool,
                                     spatial_squeeze=False,
                                     scope='resnet')
    self.assertTrue(logits.op.name.startswith('resnet/logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, 1, 1, num_classes])
    images = create_test_input(batch, height, width, 3)
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEqual(output.shape, (batch, 1, 1, num_classes))
resnet_v2_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def testAtrousFullyConvolutionalUnknownHeightWidth(self):
    batch = 2
    height, width = 65, 65
    global_pool = False
    output_stride = 8
    inputs = create_test_input(batch, None, None, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      output, _ = self._resnet_small(inputs,
                                     None,
                                     global_pool=global_pool,
                                     output_stride=output_stride)
    self.assertListEqual(output.get_shape().as_list(),
                         [batch, None, None, 32])
    images = create_test_input(batch, height, width, 3)
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(output, {inputs: images.eval()})
      self.assertEqual(output.shape, (batch, 9, 9, 32))
mobilenet_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Conv2d_13_pointwise']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
mobilenet_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
mobilenet_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
                                          reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
inception_v3_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 39 收藏 0 点赞 0 评论 0
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
inception_v3_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
inception_v4_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 45 收藏 0 点赞 0 评论 0
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000
    with self.test_session() as sess:
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      inception.inception_v4(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = inception.inception_v4(eval_inputs,
                                         num_classes,
                                         is_training=False,
                                         reuse=True)
      predictions = tf.argmax(logits, 1)
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
inception_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
inception_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 224, 224
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v1(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
layers_test.py 文件源码 项目:fold 作者: tensorflow 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def test_fractalnet_smoketest(self):
    input_placeholder = tf.placeholder(tf.float32, [None, 3])
    output_placeholder = tf.placeholder(tf.float32, [None, 3])
    fractal_net = tdl.FractalNet(3, 2, lambda name: tdl.FC(3, name=name))
    result = fractal_net(input_placeholder)
    loss = tf.nn.l2_loss(result - output_placeholder)
    optr = tf.train.GradientDescentOptimizer(0.001)
    trainer = optr.minimize(loss)

    dataset = np.random.standard_normal([10, 3])
    answers = np.random.standard_normal([10, 3])

    feed_dict = {input_placeholder: dataset, output_placeholder: answers}
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      old_loss = loss.eval(feed_dict)
      for unused_iteration in range(20):
        sess.run([trainer], feed_dict)
      new_loss = loss.eval(feed_dict)
      self.assertLess(new_loss, old_loss)


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