![]() ![]() set_title ( ax_title ) # Remove ticks from the plot. num_examples / batch_size ) global_step = 0 for epoch in range ( epochs ): print ( 'Training epoch: ". InteractiveSession () # using InteractiveSession instead of Session to test network in separate cell sess. global_variables_initializer () merged = tf. Example usage with eager execution, the default in TF 2.x: writer tf.summary.createfilewriter('/tmp/mylogs/eager') with writer. argmax ( output_logits, axis = 1, name = 'predictions' ) # Initializing the variables init = tf. scalar ( 'accuracy', accuracy ) # Network predictions cls_prediction = tf. ![]() We assign all of this to the Python variable tftensorboardwriter. We are going to write the file to the graphs directory, and what we want to write is the aph. tftensorboardwriter tf.summary.FileWriter ( './graphs', aph) So you can see tf.summary.FileWriter. argmax ( y, 1 ), name = 'correct_pred' ) accuracy = tf. To do this, we’ll use TensorFlow’s Summary FileWriter. variable_scope ( 'Accuracy' ): correct_prediction = tf. AdamOptimizer ( learning_rate = learning_rate, name = 'Adam-op' ). variable_scope ( 'Optimizer' ): optimizer = tf. softmax_cross_entropy_with_logits ( labels = y, logits = output_logits ), name = 'loss' ) tf. A summary is always much shorter than the original text. float32, shape =, name = 'Y' ) fc1 = fc_layer ( x, h1, 'Hidden_layer', use_relu = True ) output_logits = fc_layer ( fc1, n_classes, 'Output_layer', use_relu = False ) # Define the loss function, optimizer, and accuracy with tf. Summarizing, or writing a summary, means giving a concise overview of a text's main points in your own words. reshape ( x, ( - 1, img_w, img_h, 1 )), max_outputs = 5 ) y = tf. # Create graph # Placeholders for inputs (x), outputs(y) with tf. matmul ( x, W ) layer += b if use_relu : layer = tf. histogram ( 'W', W ) b = bias_variable ( name, ) tf. get_shape () W = weight_variable ( name, shape = ) tf. float32, initializer = initial ) def fc_layer ( x, num_units, name, use_relu = True ): """ Create a fully-connected layer :param x: input from previous layer :param num_units: number of hidden units in the fully-connected layer :param name: layer name :param use_relu: boolean to add ReLU non-linearity (or not) :return: The output array """ with tf. float32, shape = shape, initializer = initer ) def bias_variable ( name, shape ): """ Create a bias variable with appropriate initialization :param name: bias variable name :param shape: bias variable shape :return: initialized bias variable """ initial = tf. Assuming sess is our tf. truncated_normal_initializer ( stddev = 0.01 ) return tf. In tf1 you would create some summary ops (one op for each thing you would want to store), which you would then merge into a single op, run that merged op inside a session and then write this to a file using a FileWriter object. # weight and bais wrappers def weight_variable ( name, shape ): """ Create a weight variable with appropriate initialization :param name: weight name :param shape: weight shape :return: initialized weight variable """ initer = tf. ![]()
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