![]() If the number of training examples is high, data is processed in batches, where every batch would contain âbâ training examples in one iteration. The value âmâ refers to the total number of training examples in the dataset.The value âbâ is a value less than âmâ. Here, âbâ number of examples are processed in every iteration, where b ![]() Next, the weights are updated to ensure that the error is minimized. Once the parameters are assigned coefficients, the error or loss is calculated. To implement this, a set of parameters are defined, and they need to be minimized. Mathematically speaking, the local minimum of a function is obtained. It would be really appreciated if someone more experienced in TensorFlow can tell me what might be causing this, so I can learn to avoid it in the future.The idea behind using gradient descent is to minimize the loss when in various machine learning algorithms. Since I don't have much intuition for this, I tried various superficial things like altering the learning rate and batch size, but they didn't do anything to that perpetual 0% accuracy. The sudden jump after 500 epochs is also suspicious. It seems the model "works", but I think that something else is going on that's indicative of a larger problem. Print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))Īs can be seen, the minibatch accuracy is always 0%, but the minibatch loss is going down and the validation accuracy is going up. Print("Validation accuracy: %.1f%%" % accuracy(valid_prediction.eval(), Print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels)) Print("Minibatch loss at step %d: %f" % (step, l)) Offset = (step * batch_size) % (train_labels.shape - batch_size)Ä«atch_data = train_datasetÄ«atch_labels = train_labelsįeed_dict = Return (100.0 * np.sum(np.argmax(predictions, 1) = np.argmax(labels, 1)) / predictions.shape) With a simple runner in this fashion: num_steps = 5000 Tf.nn.relu(tf.matmul(tf_test_dataset, weights_ih) + biases_ih), Test_prediction = tf.nn.softmax(tf.matmul( Tf.nn.relu(tf.matmul(tf_valid_dataset, weights_ih) + biases_ih), Valid_prediction = tf.nn.softmax(tf.matmul( ![]() Train_prediction = tf.nn.softmax(hidden_layer_output) Optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss) Loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=tf_train_labels)) Output = tf.matmul(hidden_layer_output, weights_ho) + biases_ho Logits = tf.matmul(tf_train_dataset, weights_ih) + biases_ih Weights_ho = tf.Variable(tf.truncated_normal())Ä«iases_ho = tf.Variable(tf.zeros()) Weights_ih = tf.Variable(tf.truncated_normal())Ä«iases_ih = tf.Variable(tf.ones()/10) Tf_test_dataset = tf.constant(test_dataset) Tf_valid_dataset = tf.constant(valid_dataset) Tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels)) Shape=(batch_size, image_size * image_size)) Tf_train_dataset = tf.placeholder(tf.float32, This was how I solved it (using TensorFlow v1.0.0): batch_size = 128 In particular, I was training on the well-known notMNIST dataset, which has the exact same shape as MNIST but with more difficult examples. I'm currently very much a beginner with TensorFlow and Deep Learning in general, and I was trying to make a pretty simple 2-layer neural network with the ReLU activation function for the hidden layer, and softmax for the output layer.
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