# Layers supported in Caffe2DML

Caffe2DML to be as compatible with the Caffe specification as possible. The main differences are given below along with the usage guide that mirrors the Caffe specification.

## Vision Layers

### Convolution Layer

Invokes nn/layers/conv2d_builtin.dml or nn/layers/conv2d_depthwise.dml layer.

Required Parameters:

• num_output: the number of filters
• kernel_size (or kernel_h and kernel_w): specifies height and width of each filter

Optional Parameters:

• bias_term (default true): specifies whether to learn and apply a set of additive biases to the filter outputs
• stride (or stride_h and stride_w) (default 1): specifies the intervals at which to apply the filters to the input
• group (g) (default 1): If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the ith output group channels will be only connected to the ith input group channels. Note: we only support depthwise convolution, hence g should be divisible by number of channels

Parameters that are ignored:

• weight_filler: We use the heuristic by He et al., which limits the magnification of inputs/gradients during forward/backward passes by scaling unit-Gaussian weights by a factor of sqrt(2/n), under the assumption of relu neurons.
• bias_filler: We use constant bias_filler with value:0

Sample Usage:  layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" # learning rate and decay multipliers for the filters param { lr_mult: 1 decay_mult: 1 } # learning rate and decay multipliers for the biases param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 96 # learn 96 filters kernel_size: 11 # each filter is 11x11 stride: 4 # step 4 pixels between each filter application weight_filler { type: "xavier" # initialize the filters from a Gaussian } bias_filler { type: "constant" # initialize the biases to zero (0) value: 0 } } } 

### Pooling Layer

Invokes nn/layers/max_pool2d_builtin.dml layer.

Required Parameters:

• kernel_size (or kernel_h and kernel_w): specifies height and width of each filter

Optional Parameters: - pool (default MAX): the pooling method. Currently, we only support MAX, not AVE, or STOCHASTIC. - pad (or pad_h and pad_w) (default 0): specifies the number of pixels to (implicitly) add to each side of the input - stride (or stride_h and stride_w) (default 1): specifies the intervals at which to apply the filters to the input

Sample Usage:  layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 # pool over a 3x3 region stride: 2 # step two pixels (in the bottom blob) between pooling regions } } 

### Deconvolution Layer

Required Parameters:

• num_output: the number of filters
• kernel_size (or kernel_h and kernel_w): specifies height and width of each filter

Optional Parameters:

• bias_term (default true): specifies whether to learn and apply a set of additive biases to the filter outputs
• stride (or stride_h and stride_w) (default 1): specifies the intervals at which to apply the filters to the input
• group (g) (default 1): If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the ith output group channels will be only connected to the ith input group channels. Note: we only support depthwise convolution, hence g should be divisible by number of channels

Parameters that are ignored:

• weight_filler: We use the heuristic by He et al., which limits the magnification of inputs/gradients during forward/backward passes by scaling unit-Gaussian weights by a factor of sqrt(2/n), under the assumption of relu neurons.
• bias_filler: We use constant bias_filler with value:0

Sample Usage:  layer { name: "upconv_d5c_u4a" type: "Deconvolution" bottom: "u5d" top: "u4a" param { lr_mult: 0.0 decay_mult: 0.0 } convolution_param { num_output: 190 bias_term: false pad: 1 kernel_size: 4 group: 190 stride: 2 weight_filler { type: "bilinear" } } } 

## Common Layers

### Inner Product / Fully Connected Layer

Invokes nn/layers/affine.dml layer.

Required Parameters:

• num_output: the number of filters

Parameters that are ignored: - weight_filler (default type: ‘constant’ value: 0): We use the heuristic by He et al., which limits the magnification of inputs/gradients during forward/backward passes by scaling unit-Gaussian weights by a factor of sqrt(2/n), under the assumption of relu neurons. - bias_filler (default type: ‘constant’ value: 0): We use the default type and value. - bias_term (default true): specifies whether to learn and apply a set of additive biases to the filter outputs. We use bias_term=true.

Sample Usage:  layer { name: "fc8" type: "InnerProduct" # learning rate and decay multipliers for the weights param { lr_mult: 1 decay_mult: 1 } # learning rate and decay multipliers for the biases param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } bottom: "fc7" top: "fc8" } 

### Dropout Layer

Invokes nn/layers/dropout.dml layer.

Optional Parameters:

• dropout_ratio(default = 0.5): dropout ratio

Sample Usage:  layer { name: "drop1" type: "Dropout" bottom: "relu3" top: "drop1" dropout_param { dropout_ratio: 0.5 } } 

## Normalization Layers

### BatchNorm Layer

This is used in combination with Scale layer.

Invokes nn/layers/batch_norm2d.dml layer.

Optional Parameters: - moving_average_fraction (default = .999): Momentum value for moving averages. Typical values are in the range of [0.9, 0.999]. - eps (default = 1e-5): Smoothing term to avoid divide by zero errors. Typical values are in the range of [1e-5, 1e-3].

Parameters that are ignored: - use_global_stats: If false, normalization is performed over the current mini-batch and global statistics are accumulated (but not yet used) by a moving average. If true, those accumulated mean and variance values are used for the normalization. By default, it is set to false when the network is in the training phase and true when the network is in the testing phase.

Sample Usage:  layer { bottom: "conv1" top: "conv1" name: "bn_conv1" type: "BatchNorm" batch_norm_param { use_global_stats: true } } layer { bottom: "conv1" top: "conv1" name: "scale_conv1" type: "Scale" scale_param { bias_term: true } } 

## Activation / Neuron Layers

In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size. In the layers below, we will ignore the input and out sizes as they are identical.

### ReLU / Rectified-Linear Layer

Invokes nn/layers/relu.dml layer.

Parameters that are ignored: - negative_slope (default 0): specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.

Sample Usage:  layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } 

### TanH Layer

Invokes nn/layers/tanh.dml layer.

Sample Usage:  layer { name: "tanh1" type: "TanH" bottom: "conv1" top: "conv1" } 

### Sigmoid Layer

Invokes nn/layers/sigmoid.dml layer.

Sample Usage:  layer { name: "sigmoid1" type: "Sigmoid" bottom: "conv1" top: "conv1" } 

### Threshold Layer

Computes X > threshold

Parameters that are ignored: - threshold (default: 0):Strictly positive values

Sample Usage:  layer { name: "threshold1" type: "Threshold" bottom: "conv1" top: "conv1" } 

## Utility Layers

### Eltwise Layer

Element-wise operations such as product or sum between two blobs.

Parameters that are ignored: - operation(default: SUM): element-wise operation. only SUM supported for now. - table_prod_grad(default: true): Whether to use an asymptotically slower (for >2 inputs) but stabler method of computing the gradient for the PROD operation. (No effect for SUM op.)

Sample Usage:  layer { bottom: "res2a_branch1" bottom: "res2a_branch2c" top: "res2a" name: "res2a" type: "Eltwise" } 

### Concat Layer

Inputs: - n_i * c_i * h * w for each input blob i from 1 to K.

Outputs: - out: Outputs, of shape - if axis = 0: (n_1 + n_2 + ... + n_K) * c_1 * h * w, and all input c_i should be the same. - if axis = 1: n_1 * (c_1 + c_2 + ... + c_K) * h * w, and all input n_i should be the same.

Optional Parameters: - axis (default: 1): The axis along which to concatenate.

Sample Usage:  layer { name: "concat_d5cc_u5a-b" type: "Concat" bottom: "u5a" bottom: "d5c" top: "u5b" } 

### Softmax Layer

Invokes nn/layers/softmax.dml layer.

Computes the forward pass for a softmax classifier. The inputs are interpreted as unnormalized, log-probabilities for each of N examples, and the softmax function transforms them to normalized probabilities.

This can be interpreted as a generalization of the sigmoid function to multiple classes.

probs_ij = e^scores_ij / sum(e^scores_i)

Parameters that are ignored: - axis (default: 1): The axis along which to perform the softmax.

Sample Usage:  layer { name: "sm" type: "Softmax" bottom: "score" top: "sm" } 

## Loss Layers

Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.

### Softmax with Loss Layer

The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.

Invokes nn/layers/softmax.dml and nn/layers/cross_entropy_loss.dml for classification problems.

For image segmentation problems, invokes nn/layers/softmax2d_loss.dml layer.

Sample Usage:  layer { name: "loss" type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" top: "loss" } 

### Euclidean layer

The Euclidean loss layer computes the sum of squares of differences of its two inputs.

Invokes nn/layers/l2_loss.dml layer.

Sample Usage:  layer { name: "loss" type: "EuclideanLoss" bottom: "ip2" bottom: "label" top: "loss" } 

#### What is the purpose of Caffe2DML API ?

Most deep learning experts are more likely to be familiar with the Caffe’s specification rather than DML language. For these users, the Caffe2DML API reduces the learning curve to using SystemML. Instead of requiring the users to write a DML script for training, fine-tuning and testing the model, Caffe2DML takes as an input a network and solver specified in the Caffe specification and automatically generates the corresponding DML.

#### With Caffe2DML, does SystemML now require Caffe to be installed ?

Absolutely not. We only support Caffe’s API for convenience of the user as stated above. Since the Caffe’s API is specified in the protobuf format, we are able to generate the java parser files and donot require Caffe to be installed. This is also true for Tensorboard feature of Caffe2DML.

 Dml.g4 ---> antlr ---> DmlLexer.java, DmlListener.java, DmlParser.java ---> parse foo.dml caffe.proto ---> protoc ---> target/generated-sources/caffe/Caffe.java ---> parse caffe_network.proto, caffe_solver.proto 

Again, the SystemML engine doesnot invoke (or depend on) Caffe for any of its runtime operators. Since the grammar files for the respective APIs (i.e. caffe.proto) are used by SystemML, we include their licenses in our jar files.

#### How can I speedup the training with Caffe2DML ?

• Enable native BLAS to improve the performance of CP convolution and matrix multiplication operators. If you are using OpenBLAS, please ensure that it was built with USE_OPENMP flag turned on. For more detail see http://apache.github.io/systemml/native-backend

python caffe2dmlObject.setConfigProperty("sysml.native.blas", "auto") 

• Turn on the experimental codegen feature. This should help reduce unnecessary allocation cost after every binary operation.

python caffe2dmlObject.setConfigProperty("sysml.codegen.enabled", "true").setConfigProperty("sysml.codegen.plancache", "true") 

#### How to enable GPU support in Caffe2DML ?

To be consistent with other mllearn algorithms, we recommend that you use following method instead of setting the solver_mode in solver file.

python # The below method tells SystemML optimizer to use a GPU-enabled instruction if the operands fit in the GPU memory caffe2dmlObject.setGPU(True) # The below method tells SystemML optimizer to always use a GPU-enabled instruction irrespective of the memory requirement caffe2dmlObject.setForceGPU(True) 

#### What is lr_policy in the solver specification ?

The parameter lr_policy specifies the learning rate decay policy. Caffe2DML supports following policies: - fixed: always return base_lr. - step: return base_lr * gamma ^ (floor(iter / step)) - exp: return base_lr * gamma ^ iter - inv: return base_lr * (1 + gamma * iter) ^ (- power) - poly: the effective learning rate follows a polynomial decay, to be zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) - sigmoid: the effective learning rate follows a sigmod decay return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))

#### How to set batch size ?

Batch size is set in data_param of the Data layer:

 layer { name: "mnist" type: "Data" top: "data" top: "label" data_param { source: "mnist_train" batch_size: 64 backend: LMDB } } 

#### How to set maximum number of iterations for training ?

The maximum number of iterations can be set in the solver specification

bash # The maximum number of iterations max_iter: 2000 

#### How to set the size of the validation dataset ?

The size of the validation dataset is determined by the parameters test_iter and the batch size. For example: If the batch size is 64 and test_iter is 10, then the validation size is 640. This setting generates following DML code internally:

python num_images = nrow(y_full) BATCH_SIZE = 64 num_validation = 10 * BATCH_SIZE X = X_full[(num_validation+1):num_images,]; y = y_full[(num_validation+1):num_images,] X_val = X_full[1:num_validation,]; y_val = y_full[1:num_validation,] num_images = nrow(y) 

#### How to monitor loss via command-line ?

To monitor loss, please set following parameters in the solver specification

 # Display training loss and accuracy every 100 iterations display: 100 # Carry out validation every 500 training iterations and display validation loss and accuracy. test_iter: 10 test_interval: 500 

#### How to pass a single jpeg image to Caffe2DML for prediction ?

To convert a jpeg into NumPy matrix, you can use the pillow package and SystemML’s convertImageToNumPyArr utility function. The below pyspark code demonstrates the usage:

python from PIL import Image import systemml as sml from systemml.mllearn import Caffe2DML img_shape = (3, 224, 224) input_image = sml.convertImageToNumPyArr(Image.open(img_file_path), img_shape=img_shape) resnet = Caffe2DML(sqlCtx, solver='ResNet_50_solver.proto', weights='ResNet_50_pretrained_weights', input_shape=img_shape) resnet.predict(input_image) 

#### How to prepare a directory of jpeg images for training with Caffe2DML ?

The below pyspark code assumes that the input dataset has 2 labels cat and dogs and the filename has these labels as prefix. We iterate through the directory and convert each jpeg image into pyspark.ml.linalg.Vector using pyspark. These vectors are stored as DataFrame and randomized using Spark SQL’s orderBy(rand()) function. The DataFrame is then saved in parquet format to reduce the cost of preprocessing for repeated training.

python from systemml.mllearn import Caffe2DML from pyspark.sql import SQLContext import numpy as np import urllib, os, scipy.ndimage from pyspark.ml.linalg import Vectors from pyspark import StorageLevel import systemml as sml from pyspark.sql.functions import rand # ImageNet specific parameters img_shape = (3, 224, 224) train_dir = '/home/biuser/dogs_vs_cats/train' def getLabelFeatures(filename): from PIL import Image vec = Vectors.dense(sml.convertImageToNumPyArr(Image.open(os.path.join(train_dir, filename)), img_shape=img_shape)[0,:]) if filename.lower().startswith('cat'): return (1, vec) elif filename.lower().startswith('dog'): return (2, vec) else: raise ValueError('Expected the filename to start with either cat or dog') list_jpeg_files = os.listdir(train_dir) # 10 files per partition train_df = sc.parallelize(list_jpeg_files, int(len(list_jpeg_files)/10)).map(lambda filename : getLabelFeatures(filename)).toDF(['label', 'features']).orderBy(rand()) # Optional: but helps seperates conversion-related from training # Alternatively, this dataframe can be passed directly to caffe2dml_model.fit(train_df) train_df.write.parquet('kaggle-cats-dogs.parquet') 

An alternative way to load images into a PySpark DataFrame for prediction, is to use MLLib’s LabeledPoint class:

python list_jpeg_files = os.listdir(train_dir) train_df = sc.parallelize(list_jpeg_files, int(len(list_jpeg_files)/10)).map(lambda filename : LabeledPoint(0, sml.convertImageToNumPyArr(Image.open(os.path.join(train_dir, filename)), img_shape=img_shape)[0,:])).toDF().select('features') # Note: convertVectorColumnsToML has an additional serialization cost train_df = MLUtils.convertVectorColumnsToML(train_df) 

#### Can I use Caffe2DML via Scala ?

Though we recommend using Caffe2DML via its Python interfaces, it is possible to use it by creating an object of the class org.apache.sysml.api.dl.Caffe2DML. It is important to note that Caffe2DML’s scala API is packaged in systemml-*-extra.jar.

#### How can I get summary information of my network ?

python lenet.summary() 

Output:

 +-----+---------------+--------------+------------+---------+-----------+---------+ | Name| Type| Output| Weight| Bias| Top| Bottom| +-----+---------------+--------------+------------+---------+-----------+---------+ |mnist| Data| (, 1, 28, 28)| | |mnist,mnist| | |conv1| Convolution|(, 32, 28, 28)| [32 X 25]| [32 X 1]| conv1| mnist| |relu1| ReLU|(, 32, 28, 28)| | | relu1| conv1| |pool1| Pooling|(, 32, 14, 14)| | | pool1| relu1| |conv2| Convolution|(, 64, 14, 14)| [64 X 800]| [64 X 1]| conv2| pool1| |relu2| ReLU|(, 64, 14, 14)| | | relu2| conv2| |pool2| Pooling| (, 64, 7, 7)| | | pool2| relu2| | ip1| InnerProduct| (, 512, 1, 1)|[3136 X 512]|[1 X 512]| ip1| pool2| |relu3| ReLU| (, 512, 1, 1)| | | relu3| ip1| |drop1| Dropout| (, 512, 1, 1)| | | drop1| relu3| | ip2| InnerProduct| (, 10, 1, 1)| [512 X 10]| [1 X 10]| ip2| drop1| | loss|SoftmaxWithLoss| (, 10, 1, 1)| | | loss|ip2,mnist| +-----+---------------+--------------+------------+---------+-----------+---------+ 

#### How can I view the script generated by Caffe2DML ?

To view the generated DML script (and additional debugging information), please set the debug parameter to True.

python lenet.set(debug=True) 

Output:  001|debug = TRUE 002|source(“nn/layers/softmax.dml”) as softmax 003|source(“nn/layers/cross_entropy_loss.dml”) as cross_entropy_loss 004|source(“nn/layers/conv2d_builtin.dml”) as conv2d_builtin 005|source(“nn/layers/relu.dml”) as relu 006|source(“nn/layers/max_pool2d_builtin.dml”) as max_pool2d_builtin 007|source(“nn/layers/affine.dml”) as affine 008|source(“nn/layers/dropout.dml”) as dropout 009|source(“nn/optim/sgd_momentum.dml”) as sgd_momentum 010|source(“nn/layers/l2_reg.dml”) as l2_reg 011|X_full_path = ifdef($X, “ “) 012|X_full = read(X_full_path) 013|y_full_path = ifdef($y, “ “) 014|y_full = read(y_full_path) 015|num_images = nrow(y_full) 016|# Convert to one-hot encoding (Assumption: 1-based labels) 017|y_full = table(seq(1,num_images,1), y_full, num_images, 10) 018|weights = ifdef(\$weights, “ “) 019|# Initialize the layers and solvers 020|X_full = X_full * 0.00390625 021|BATCH_SIZE = 64 022|[conv1_weight,conv1_bias] = conv2d_builtin::init(32,1,5,5) 023|[conv2_weight,conv2_bias] = conv2d_builtin::init(64,32,5,5) 024|[ip1_weight,ip1_bias] = affine::init(3136,512) 025|[ip2_weight,ip2_bias] = affine::init(512,10) 026|conv1_weight_v = sgd_momentum::init(conv1_weight) 027|conv1_bias_v = sgd_momentum::init(conv1_bias) 028|conv2_weight_v = sgd_momentum::init(conv2_weight) 029|conv2_bias_v = sgd_momentum::init(conv2_bias) 030|ip1_weight_v = sgd_momentum::init(ip1_weight) 031|ip1_bias_v = sgd_momentum::init(ip1_bias) 032|ip2_weight_v = sgd_momentum::init(ip2_weight) 033|ip2_bias_v = sgd_momentum::init(ip2_bias) 034|num_validation = 10 * BATCH_SIZE 035|# Sanity check to ensure that validation set is not too large 036|if(num_validation > ceil(0.3 * num_images)) { 037| max_test_iter = floor(ceil(0.3 * num_images) / BATCH_SIZE) 038| stop(“Too large validation size. Please reduce test_iter to “ + max_test_iter) 039|} 040|X = X_full[(num_validation+1):num_images,]; y = y_full[(num_validation+1):num_images,]; X_val = X_full[1:num_validation,]; y_val = y_full[1:num_validation,]; num_images = nrow(y) 041|num_iters_per_epoch = ceil(num_images / BATCH_SIZE) 042|max_epochs = ceil(2000 / num_iters_per_epoch) 043|iter = 0 044|lr = 0.01 045|for(e in 1:max_epochs) { 046| for(i in 1:num_iters_per_epoch) { 047| beg = ((i-1) * BATCH_SIZE) %% num_images + 1; end = min(beg + BATCH_SIZE - 1, num_images); Xb = X[beg:end,]; yb = y[beg:end,]; 048| iter = iter + 1 049| # Perform forward pass 050| [out3,ignoreHout_3,ignoreWout_3] = conv2d_builtin::forward(Xb,conv1_weight,conv1_bias,1,28,28,5,5,1,1,2,2) 051| out4 = relu::forward(out3) 052| [out5,ignoreHout_5,ignoreWout_5] = max_pool2d_builtin::forward(out4,32,28,28,2,2,2,2,0,0) 053| [out6,ignoreHout_6,ignoreWout_6] = conv2d_builtin::forward(out5,conv2_weight,conv2_bias,32,14,14,5,5,1,1,2,2) 054| out7 = relu::forward(out6) 055| [out8,ignoreHout_8,ignoreWout_8] = max_pool2d_builtin::forward(out7,64,14,14,2,2,2,2,0,0) 056| out9 = affine::forward(out8,ip1_weight,ip1_bias) 057| out10 = relu::forward(out9) 058| [out11,mask11] = dropout::forward(out10,0.5,-1) 059| out12 = affine::forward(out11,ip2_weight,ip2_bias) 060| out13 = softmax::forward(out12) 061| # Perform backward pass 062| dProbs = cross_entropy_loss::backward(out13,yb); dOut13 = softmax::backward(dProbs,out12); dOut13_12 = dOut13; dOut13_2 = dOut13; 063| [dOut12,ip2_dWeight,ip2_dBias] = affine::backward(dOut13_12,out11,ip2_weight,ip2_bias); dOut12_11 = dOut12; 064| dOut11 = dropout::backward(dOut12_11,out10,0.5,mask11); dOut11_10 = dOut11; 065| dOut10 = relu::backward(dOut11_10,out9); dOut10_9 = dOut10; 066| [dOut9,ip1_dWeight,ip1_dBias] = affine::backward(dOut10_9,out8,ip1_weight,ip1_bias); dOut9_8 = dOut9; 067| dOut8 = max_pool2d_builtin::backward(dOut9_8,7,7,out7,64,14,14,2,2,2,2,0,0); dOut8_7 = dOut8; 068| dOut7 = relu::backward(dOut8_7,out6); dOut7_6 = dOut7; 069| [dOut6,conv2_dWeight,conv2_dBias] = conv2d_builtin::backward(dOut7_6,14,14,out5,conv2_weight,conv2_bias,32,14,14,5,5,1,1,2,2); dOut6_5 = dOut6; 070| dOut5 = max_pool2d_builtin::backward(dOut6_5,14,14,out4,32,28,28,2,2,2,2,0,0); dOut5_4 = dOut5; 071| dOut4 = relu::backward(dOut5_4,out3); dOut4_3 = dOut4; 072| [dOut3,conv1_dWeight,conv1_dBias] = conv2d_builtin::backward(dOut4_3,28,28,Xb,conv1_weight,conv1_bias,1,28,28,5,5,1,1,2,2); dOut3_2 = dOut3; 073| # Update the parameters 074| conv1_dWeight_reg = l2_reg::backward(conv1_weight, 5.000000237487257E-4) 075| conv1_dWeight = conv1_dWeight + conv1_dWeight_reg 076| [conv1_weight,conv1_weight_v] = sgd_momentum::update(conv1_weight,conv1_dWeight,(lr * 1.0),0.8999999761581421,conv1_weight_v) 077| [conv1_bias,conv1_bias_v] = sgd_momentum::update(conv1_bias,conv1_dBias,(lr * 2.0),0.8999999761581421,conv1_bias_v) 078| conv2_dWeight_reg = l2_reg::backward(conv2_weight, 5.000000237487257E-4) 079| conv2_dWeight = conv2_dWeight + conv2_dWeight_reg 080| [conv2_weight,conv2_weight_v] = sgd_momentum::update(conv2_weight,conv2_dWeight,(lr * 1.0),0.8999999761581421,conv2_weight_v) 081| [conv2_bias,conv2_bias_v] = sgd_momentum::update(conv2_bias,conv2_dBias,(lr * 2.0),0.8999999761581421,conv2_bias_v) 082| ip1_dWeight_reg = l2_reg::backward(ip1_weight, 5.000000237487257E-4) 083| ip1_dWeight = ip1_dWeight + ip1_dWeight_reg 084| [ip1_weight,ip1_weight_v] = sgd_momentum::update(ip1_weight,ip1_dWeight,(lr * 1.0),0.8999999761581421,ip1_weight_v) 085| [ip1_bias,ip1_bias_v] = sgd_momentum::update(ip1_bias,ip1_dBias,(lr * 2.0),0.8999999761581421,ip1_bias_v) 086| ip2_dWeight_reg = l2_reg::backward(ip2_weight, 5.000000237487257E-4) 087| ip2_dWeight = ip2_dWeight + ip2_dWeight_reg 088| [ip2_weight,ip2_weight_v] = sgd_momentum::update(ip2_weight,ip2_dWeight,(lr * 1.0),0.8999999761581421,ip2_weight_v) 089| [ip2_bias,ip2_bias_v] = sgd_momentum::update(ip2_bias,ip2_dBias,(lr * 2.0),0.8999999761581421,ip2_bias_v) 090| # Compute training loss & accuracy 091| if(iter %% 100 == 0) { 092| loss = 0 093| accuracy = 0 094| tmp_loss = cross_entropy_loss::forward(out13,yb) 095| loss = loss + tmp_loss 096| true_yb = rowIndexMax(yb) 097| predicted_yb = rowIndexMax(out13) 098| accuracy = mean(predicted_yb == true_yb)100 099| training_loss = loss 100| training_accuracy = accuracy 101| print(“Iter:” + iter + “, training loss:” + training_loss + “, training accuracy:” + training_accuracy) 102| if(debug) { 103| num_rows_error_measures = min(10, ncol(yb)) 104| error_measures = matrix(0, rows=num_rows_error_measures, cols=5) 105| for(class_i in 1:num_rows_error_measures) { 106| tp = sum( (true_yb == predicted_yb) * (true_yb == class_i) ) 107| tp_plus_fp = sum( (predicted_yb == class_i) ) 108| tp_plus_fn = sum( (true_yb == class_i) ) 109| precision = tp / tp_plus_fp 110| recall = tp / tp_plus_fn 111| f1Score = 2precisionrecall / (precision+recall) 112| error_measures[class_i,1] = class_i 113| error_measures[class_i,2] = precision 114| error_measures[class_i,3] = recall 115| error_measures[class_i,4] = f1Score 116| error_measures[class_i,5] = tp_plus_fn 117| } 118| print(“class \tprecision\trecall \tf1-score\tnum_true_labels\n” + toString(error_measures, decimal=7, sep=”\t”)) 119| } 120| } 121| # Compute validation loss & accuracy 122| if(iter %% 500 == 0) { 123| loss = 0 124| accuracy = 0 125| validation_loss = 0 126| validation_accuracy = 0 127| for(iVal in 1:num_iters_per_epoch) { 128| beg = ((iVal-1) * BATCH_SIZE) %% num_validation + 1; end = min(beg + BATCH_SIZE - 1, num_validation); Xb = X_val[beg:end,]; yb = y_val[beg:end,]; 129| # Perform forward pass 130| [out3,ignoreHout_3,ignoreWout_3] = conv2d_builtin::forward(Xb,conv1_weight,conv1_bias,1,28,28,5,5,1,1,2,2) 131| out4 = relu::forward(out3) 132| [out5,ignoreHout_5,ignoreWout_5] = max_pool2d_builtin::forward(out4,32,28,28,2,2,2,2,0,0) 133| [out6,ignoreHout_6,ignoreWout_6] = conv2d_builtin::forward(out5,conv2_weight,conv2_bias,32,14,14,5,5,1,1,2,2) 134| out7 = relu::forward(out6) 135| [out8,ignoreHout_8,ignoreWout_8] = max_pool2d_builtin::forward(out7,64,14,14,2,2,2,2,0,0) 136| out9 = affine::forward(out8,ip1_weight,ip1_bias) 137| out10 = relu::forward(out9) 138| [out11,mask11] = dropout::forward(out10,0.5,-1) 139| out12 = affine::forward(out11,ip2_weight,ip2_bias) 140| out13 = softmax::forward(out12) 141| tmp_loss = cross_entropy_loss::forward(out13,yb) 142| loss = loss + tmp_loss 143| true_yb = rowIndexMax(yb) 144| predicted_yb = rowIndexMax(out13) 145| accuracy = mean(predicted_yb == true_yb)100 146| validation_loss = validation_loss + loss 147| validation_accuracy = validation_accuracy + accuracy 148| } 149| validation_accuracy = validation_accuracy / num_iters_per_epoch 150| print(“Iter:” + iter + “, validation loss:” + validation_loss + “, validation accuracy:” + validation_accuracy) 151| } 152| } 153| # Learning rate 154| lr = (0.009999999776482582 * 0.949999988079071^e) 155|}

Iter:100, training loss:0.24014199350958168, training accuracy:87.5 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 8.0000000 3.0000000 0.8888889 0.8888889 0.8888889 9.0000000 4.0000000 0.7500000 0.7500000 0.7500000 4.0000000 5.0000000 0.7500000 1.0000000 0.8571429 3.0000000 6.0000000 0.8333333 1.0000000 0.9090909 5.0000000 7.0000000 1.0000000 1.0000000 1.0000000 8.0000000 8.0000000 0.8571429 0.7500000 0.8000000 8.0000000 9.0000000 1.0000000 0.5714286 0.7272727 7.0000000 10.0000000 0.7272727 0.8888889 0.8000000 9.0000000

Iter:200, training loss:0.09555593867171894, training accuracy:98.4375 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 10.0000000 2.0000000 1.0000000 1.0000000 1.0000000 3.0000000 3.0000000 1.0000000 1.0000000 1.0000000 9.0000000 4.0000000 1.0000000 1.0000000 1.0000000 6.0000000 5.0000000 1.0000000 1.0000000 1.0000000 7.0000000 6.0000000 1.0000000 1.0000000 1.0000000 8.0000000 7.0000000 1.0000000 0.6666667 0.8000000 3.0000000 8.0000000 1.0000000 1.0000000 1.0000000 9.0000000 9.0000000 0.8571429 1.0000000 0.9230769 6.0000000 10.0000000 1.0000000 1.0000000 1.0000000 3.0000000

Iter:300, training loss:0.058686794512570216, training accuracy:98.4375 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 6.0000000 2.0000000 1.0000000 1.0000000 1.0000000 9.0000000 3.0000000 1.0000000 1.0000000 1.0000000 4.0000000 4.0000000 1.0000000 1.0000000 1.0000000 8.0000000 5.0000000 1.0000000 1.0000000 1.0000000 6.0000000 6.0000000 1.0000000 0.8750000 0.9333333 8.0000000 7.0000000 1.0000000 1.0000000 1.0000000 5.0000000 8.0000000 1.0000000 1.0000000 1.0000000 2.0000000 9.0000000 0.8888889 1.0000000 0.9411765 8.0000000 10.0000000 1.0000000 1.0000000 1.0000000 8.0000000

Iter:400, training loss:0.08742103541529415, training accuracy:96.875 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 6.0000000 2.0000000 0.8000000 1.0000000 0.8888889 8.0000000 3.0000000 1.0000000 0.8333333 0.9090909 6.0000000 4.0000000 1.0000000 1.0000000 1.0000000 4.0000000 5.0000000 1.0000000 1.0000000 1.0000000 4.0000000 6.0000000 1.0000000 1.0000000 1.0000000 6.0000000 7.0000000 1.0000000 1.0000000 1.0000000 7.0000000 8.0000000 1.0000000 1.0000000 1.0000000 6.0000000 9.0000000 1.0000000 1.0000000 1.0000000 4.0000000 10.0000000 1.0000000 0.9230769 0.9600000 13.0000000

Iter:500, training loss:0.05873836245880005, training accuracy:98.4375 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 5.0000000 3.0000000 1.0000000 1.0000000 1.0000000 6.0000000 4.0000000 1.0000000 1.0000000 1.0000000 9.0000000 5.0000000 1.0000000 1.0000000 1.0000000 4.0000000 6.0000000 1.0000000 0.8571429 0.9230769 7.0000000 7.0000000 0.8571429 1.0000000 0.9230769 6.0000000 8.0000000 1.0000000 1.0000000 1.0000000 9.0000000 9.0000000 1.0000000 1.0000000 1.0000000 10.0000000 10.0000000 1.0000000 1.0000000 1.0000000 5.0000000

Iter:500, validation loss:260.1580978627665, validation accuracy:96.43954918032787 Iter:600, training loss:0.07584116043829209, training accuracy:98.4375 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 8.0000000 2.0000000 1.0000000 1.0000000 1.0000000 4.0000000 3.0000000 1.0000000 1.0000000 1.0000000 4.0000000 4.0000000 1.0000000 1.0000000 1.0000000 4.0000000 5.0000000 1.0000000 1.0000000 1.0000000 5.0000000 6.0000000 1.0000000 1.0000000 1.0000000 8.0000000 7.0000000 1.0000000 1.0000000 1.0000000 8.0000000 8.0000000 1.0000000 0.9230769 0.9600000 13.0000000 9.0000000 1.0000000 1.0000000 1.0000000 5.0000000 10.0000000 0.8333333 1.0000000 0.9090909 5.0000000

Iter:700, training loss:0.07973166944626336, training accuracy:98.4375 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 5.0000000 2.0000000 1.0000000 1.0000000 1.0000000 4.0000000 3.0000000 1.0000000 1.0000000 1.0000000 6.0000000 4.0000000 1.0000000 1.0000000 1.0000000 4.0000000 5.0000000 1.0000000 1.0000000 1.0000000 5.0000000 6.0000000 1.0000000 1.0000000 1.0000000 6.0000000 7.0000000 1.0000000 1.0000000 1.0000000 10.0000000 8.0000000 0.8000000 1.0000000 0.8888889 4.0000000 9.0000000 1.0000000 1.0000000 1.0000000 8.0000000 10.0000000 1.0000000 0.9166667 0.9565217 12.0000000

Iter:800, training loss:0.0063778595034221855, training accuracy:100.0 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 9.0000000 2.0000000 1.0000000 1.0000000 1.0000000 6.0000000 3.0000000 1.0000000 1.0000000 1.0000000 7.0000000 4.0000000 1.0000000 1.0000000 1.0000000 7.0000000 5.0000000 1.0000000 1.0000000 1.0000000 4.0000000 6.0000000 1.0000000 1.0000000 1.0000000 9.0000000 7.0000000 1.0000000 1.0000000 1.0000000 6.0000000 8.0000000 1.0000000 1.0000000 1.0000000 8.0000000 9.0000000 1.0000000 1.0000000 1.0000000 2.0000000 10.0000000 1.0000000 1.0000000 1.0000000 6.0000000

Iter:900, training loss:0.019673112167879484, training accuracy:100.0 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 4.0000000 3.0000000 1.0000000 1.0000000 1.0000000 3.0000000 4.0000000 1.0000000 1.0000000 1.0000000 5.0000000 5.0000000 1.0000000 1.0000000 1.0000000 6.0000000 6.0000000 1.0000000 1.0000000 1.0000000 10.0000000 7.0000000 1.0000000 1.0000000 1.0000000 7.0000000 8.0000000 1.0000000 1.0000000 1.0000000 7.0000000 9.0000000 1.0000000 1.0000000 1.0000000 12.0000000 10.0000000 1.0000000 1.0000000 1.0000000 7.0000000

Iter:1000, training loss:0.06137978002508307, training accuracy:96.875 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 5.0000000 2.0000000 1.0000000 1.0000000 1.0000000 7.0000000 3.0000000 1.0000000 1.0000000 1.0000000 8.0000000 4.0000000 0.8333333 0.8333333 0.8333333 6.0000000 5.0000000 1.0000000 1.0000000 1.0000000 5.0000000 6.0000000 1.0000000 1.0000000 1.0000000 10.0000000 7.0000000 1.0000000 1.0000000 1.0000000 3.0000000 8.0000000 0.8888889 0.8888889 0.8888889 9.0000000 9.0000000 1.0000000 1.0000000 1.0000000 7.0000000 10.0000000 1.0000000 1.0000000 1.0000000 4.0000000

Iter:1000, validation loss:238.62301345198944, validation accuracy:97.02868852459017 Iter:1100, training loss:0.023325103696013115, training accuracy:100.0 class precision recall f1-score num_true_labels 1.0000000 1.0000000 1.0000000 1.0000000 4.0000000 2.0000000 1.0000000 1.0000000 1.0000000 10.0000000 3.0000000 1.0000000 1.0000000 1.0000000 6.0000000 4.0000000 1.0000000 1.0000000 1.0000000 4.0000000 5.0000000 1.0000000 1.0000000 1.0000000 2.0000000 6.0000000 1.0000000 1.0000000 1.0000000 10.0000000 7.0000000 1.0000000 1.0000000 1.0000000 7.0000000 8.0000000 1.0000000 1.0000000 1.0000000 6.0000000 9.0000000 1.0000000 1.0000000 1.0000000 9.0000000 10.0000000 1.0000000 1.0000000 1.0000000 6.0000000 …