• burnpredlibvahurtr

Fastgsm Dbx Client 2.22.0







A: You can get data from a Kaggle notebook and I can see that there is some code in this notebook that might be useful to you import tensorflow as tf # Number of distinct training examples. num_examples = 6 # Number of unique image features. num_features = 10 # Number of classes. num_classes = 7 # Size of a single minibatch entry. sample_batch_size = 10 # Training Epochs. train_steps = 500000 # Learning rate decay. momentum = 0.9 # Minimum validation score for stopping training. valid_steps = 1000 # Number of evaluation images per training example. num_eval_images = 1 # Random seed. random_seed = 17 # Evaluate image features from training data. # A vector of indices in which elements correspond to image features. eval_feature_indices = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] # Evaluate image features from validation data. # Only the index at which the validation score is calculated # is used. valid_feature_indices = [8] # Determines the type of image features to evaluate. eval_feature_type = tf.contrib.layers.real_valued_column("image_features") # Initialise Variables. # Initialise variables for minibatch examples. # The data tensor is not used, so it can be set to a very large value. global_step = tf.Variable(0, trainable=False) # Store feature vectors in minibatch entries. # Note the use of a list of indices for evaluation data. train_minibatch_features = tf.contrib.layers.batch_features( tf.constant(train_features), sample_batch_size) valid_minibatch_features = tf.contrib.layers.batch_features( tf.constant(valid_features), sample_batch_size


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