def setUp(self): super(MultiplexerDataProviderTest, self).setUp() self.logdir = self.get_temp_dir() logdir = os.path.join(self.logdir, "polynomials") with tf.summary.create_file_writer(logdir).as_default(): for i in xrange(10): scalar_summary.scalar("square", i ** 2, step=2 * i, description="boxen") scalar_summary.scalar("cube", i ** 3, step=3 * i) logdir = os.path.join(self.logdir, "waves") with tf.summary.create_file_writer(logdir).as_default(): for i in xrange(10): scalar_summary.scalar("sine", tf.sin(float(i)), step=i) scalar_summary.scalar("square", tf.sign(tf.sin(float(i))), step=i) # Summary with rank-0 data but not owned by the scalars plugin. metadata = summary_pb2.SummaryMetadata() metadata.plugin_data.plugin_name = "marigraphs" tf.summary.write("high_tide", tensor=i, step=i, metadata=metadata) logdir = os.path.join(self.logdir, "pictures") with tf.summary.create_file_writer(logdir).as_default(): purple = tf.constant([[[255, 0, 255]]], dtype=tf.uint8) for i in xrange(1, 11): image_summary.image("purple", [tf.tile(purple, [i, i, 1])], step=i)
def f(): def body(step, tokens): next_token = random_ops.random_uniform([bsz]) tokens = tokens.write(step, next_token) return (step + 1, tokens) def cond(step, tokens): del tokens return math_ops.less(step, max_length) tokens_var = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=max_length, dynamic_size=False, clear_after_read=False, element_shape=(bsz, ), name="tokens_accumulator", ) step = constant_op.constant(0) step, tokens_var = control_flow_ops.while_loop( cond, body, [step, tokens_var]) image_flat = array_ops.transpose(tokens_var.stack(), [1, 0]) image = array_ops.tile( array_ops.reshape(image_flat, [bsz, 32, 32, 1]), [1, 1, 1, 3]) image_summary_v2.image( "image_sample", image, constant_op.constant(5, dtype=dtypes.int64))
def call(self, x): # Add summary image using compat v2 implementation. image_summary_v2.image('custom_image_summary_v2', x) return x
def setUp(self): super(MultiplexerDataProviderTest, self).setUp() self.logdir = self.get_temp_dir() self.ctx = context.RequestContext() logdir = os.path.join(self.logdir, "polynomials") with tf.summary.create_file_writer(logdir).as_default(): for i in range(10): scalar_summary.scalar( "square", i ** 2, step=2 * i, description="boxen" ) scalar_summary.scalar("cube", i ** 3, step=3 * i) logdir = os.path.join(self.logdir, "waves") with tf.summary.create_file_writer(logdir).as_default(): for i in range(10): scalar_summary.scalar("sine", tf.sin(float(i)), step=i) scalar_summary.scalar( "square", tf.sign(tf.sin(float(i))), step=i ) # Summary with rank-0 data but not owned by the scalars plugin. metadata = summary_pb2.SummaryMetadata() metadata.plugin_data.plugin_name = "marigraphs" metadata.data_class = summary_pb2.DATA_CLASS_SCALAR tf.summary.write( "high_tide", tensor=i, step=i, metadata=metadata ) # Summary with rank-1 data of scalar data class (bad!). metadata = summary_pb2.SummaryMetadata() metadata.plugin_data.plugin_name = "greetings" metadata.data_class = summary_pb2.DATA_CLASS_SCALAR tf.summary.write( "bad", tensor=[i, i], step=i, metadata=metadata ) logdir = os.path.join(self.logdir, "lebesgue") with tf.summary.create_file_writer(logdir).as_default(): data = [ ("very smooth", (0.0, 0.25, 0.5, 0.75, 1.0), "uniform"), ("very smoothn't", (0.0, 0.01, 0.99, 1.0), "bimodal"), ] for (description, distribution, name) in data: tensor = tf.constant([distribution], dtype=tf.float64) for i in range(1, 11): histogram_summary.histogram( name, tensor * i, step=i, description=description ) logdir = os.path.join(self.logdir, "mondrian") with tf.summary.create_file_writer(logdir).as_default(): data = [ ("red", (221, 28, 38), "top-right"), ("blue", (1, 91, 158), "bottom-left"), ("yellow", (239, 220, 111), "bottom-right"), ] for (name, color, description) in data: image_1x1 = tf.constant([[[color]]], dtype=tf.uint8) for i in range(1, 11): # Use a non-monotonic sequence of sample sizes to # test `max_length` calculation. k = 6 - abs(6 - i) # 1, .., 6, .., 2 # a `k`-sample image summary of `i`-by-`i` images image = tf.tile(image_1x1, [k, i, i, 1]) image_summary.image( name, image, step=i, description=description, max_outputs=99, )