![]() ![]() truncated_normal ( shape, stddev = 0.1 ) return tf. image ( 'input', image_shaped_input, 10 ) # We can't initialize these variables to 0 - the network will get stuck.ĭef weight_variable ( shape ): """Create a weight variable with appropriate initialization.""" initial = tf. name_scope ( 'input_reshape' ): image_shaped_input = tf. InteractiveSession () # Create a multilayer model. data_dir, one_hot = True, fake_data = FLAGS. """ from _future_ import absolute_import from _future_ import division from _future_ import print_function import argparse import os import sys z import tensorflow as tf from import input_data FLAGS = None def train (): # Import data It demonstrates the functionality of every TensorBoard dashboard. Naming summary tags so that they are grouped meaningfully in TensorBoard. Tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of This is an unimpressive MNIST model, but it is a good example of using """A simple MNIST classifier which displays summaries in TensorBoard. # See the License for the specific language governing permissions and # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # distributed under the License is distributed on an 'AS IS' BASIS, # Unless required by applicable law or agreed to in writing, software # You may obtain a copy of the License at # you may not use this file except in compliance with the License. # Licensed under the Apache License, Version 2.0 (the 'License') ![]()
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