SGD, RMSProp, Adam, Momentum, AdaGrad, Ftrl, AdaDelta Softmax_categorical_crossentropy, categorical_crossentropy, binary_crossentropy, mean_square, hinge_loss, roc_auc_score, weak_cross_entropy_2d Linear, tanh, sigmoid, softmax, softplus, softsign, relu, relu6, leaky_relu, prelu, elu In practice, just providing the op name as argument is enough (such as activation='relu' or regularizer='L2' for conv_2d), but a function can also be provided for further customization. These ops are firstly mean to be part of the above 'layers' arguments, but they can also be used independently in any other Tensorflow graph for convenience. Simple_rnn, lstm, gru, bidirectionnal_rnn, dynamic_rnnīatch_normalization, local_response_normalization, l2_normalizeīesides layers concept, TFLearn also provides many different ops to be used when building a neural network. Input_data, fully_connected, dropout, custom_layer, reshape, flatten, activation, single_unit, highway, one_hot_encoding, time_distributedĬonv_2d, conv_2d_transpose, max_pool_2d, avg_pool_2d, upsample_2d, conv_1d, max_pool_1d, avg_pool_1d, residual_block, residual_bottleneck, conv_3d, max_pool_3d, avg_pool_3d, highway_conv_1d, highway_conv_2d, global_avg_pool, global_max_pool Here is a list of all currently available layers: File While in TFLearn, it only takes a line: nv_2d(x, 32, 5, activation='relu', name='conv1') X = tf.nn.conv2d(x, W, strides=, padding='SAME') W = tf.Variable(tf.random_normal(), dtype=tf.float32, name='Weights')ī = tf.Variable(tf.random_normal(), dtype=tf.float32, name='biases') In Tensorflow, writing these kinds of operations can be quite tedious: with tf.name_scope('conv1'): Add an activation function after the convolution.Create and initialize weights and biases variables.While completely defining a model using Tensorflow ops can be time consuming and repetitive, TFLearn brings 'layers' that represent an abstract set of operations to make building neural networks more convenient. This API is intuitive and fully compatible with Tensorflow. TFLearn introduces a High-Level API that makes neural network building and training fast and easy. First, highlighting TFLearn high-level API for fast neural network building and training, and then showing how TFLearn layers, built-in ops and helpers can directly benefit any model implementation with Tensorflow.

Here is a basic guide that introduces TFLearn and its functionalities. Data Preprocessing and Data Augmentation.

Model builder precondition