mnist_labels.append(mnist_label[:n_labels]) return mnist_features, mnist_labels # Number of features (28*28 image is 784 features) n_features = 784 # Number of labels n_labels = 3 # Features and Labels features = tf.placeholder(tf.float32) labels = tf.placeholder(tf.float32) # Weights and Biases w = get_weights(n_features, n_labels) b = get_biases(n_labels) # Linear Function xW + b logits = linear(features, w, b) # Training data train_features, train_labels = mnist_features_labels(n_labels) with tf.Session() as session: session.run(tf.global_variables_initializer()) # Softmax functiond to normalize the varables from 0 to 1 # variables with heavier weights weigh more than the ones with lighter weight prediction = tf.nn.softmax(logits)
mnist_labels.append(mnist_label[:n_labels]) return mnist_features, mnist_labels # Number of features (28*28 image is 784 features) n_features = 784 # Number of labels n_labels = 3 # Features and Labels features = tf.placeholder(tf.float32) labels = tf.placeholder(tf.float32) # Weights and Biases w = get_weights(n_features, n_labels) b = get_biases(n_labels) # Linear Function xW + b logits = linear(features, w, b) # Training data train_features, train_labels = mnist_features_labels(n_labels) with tf.Session() as session: # TODO: Initialize session variables # Softmax prediction = tf.nn.softmax(logits) # Cross entropy