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4
settings/FASHION_MNIST/CNN_Approx/dataset.json
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settings/FASHION_MNIST/CNN_Approx/dataset.json
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{
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"data_path": "./DATA_FASHION_MNIST/",
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"data_mode": "MNIST_FASHION"
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}
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settings/FASHION_MNIST/CNN_Approx/def.json
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settings/FASHION_MNIST/CNN_Approx/def.json
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{
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"epoch_id_max": 200,
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"batch_size": 24,
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"stage_id": 0,
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"simulation_id": 0,
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"learning_parameters": {
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"loss_mode": 1, // X-Entropy
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"number_of_batches_for_one_update": 20,
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"learning_rate_gamma_w": 0.001,
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"lr_scheduler_patience_w": -1,
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"adapt_learning_rate_after_minibatch": false,
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"w_trainable": [
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true
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]
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},
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"augmentation": {},
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"image_statistics": {},
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"approximation_setting": {
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"number_of_frac_bits": [
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30
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], // Quantization of the mantissa
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"approximation_enable": [
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true
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], // In addition to the quantization more approx
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"number_of_trunc_bits": [
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1
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]
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}
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}
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settings/FASHION_MNIST/CNN_Approx/network.json
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settings/FASHION_MNIST/CNN_Approx/network.json
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{
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"network_structure": {
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"number_of_output_neurons": 10,
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"layer_type": [
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"APPROX CONV2D",
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"RELU",
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"MAX POOLING",
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"APPROX CONV2D",
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"RELU",
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"MAX POOLING",
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"APPROX CONV2D",
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"RELU",
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"APPROX CONV2D"
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],
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"strides": [
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[
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1,
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1
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], // "CONV2D"
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[], // "RELU",
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[
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2,
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2
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], // "MAX POOLING",
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[
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1,
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1
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], // "CONV2D"
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[], // "RELU",
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[
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2,
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2
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], // "MAX POOLING",
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[
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1,
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1
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], // "CONV2D"
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[], // "RELU",
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[
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1,
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1
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] // "CONV2D"
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],
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"forward_neuron_numbers": [
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[
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1,
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32
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], // "CONV2D",
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[], // "RELU",
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[], // "MAX POOLING",
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[
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32,
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64
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], // "CONV2D",
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[], // "RELU",
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[], // "MAX POOLING",
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[
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64,
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96
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], // "CONV2D",
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[], // "RELU",
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[
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96,
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10
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] // "CONV2D",
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],
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"forward_kernel_size": [
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[
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5,
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5
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], // "CONV2D",
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[], // "RELU",
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[
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2,
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2
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], // "MAX POOLING",
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[
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5,
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5
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], // "CONV2D",
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[], // "RELU",
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[
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2,
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2
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], // "MAX POOLING",
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[
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3,
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3
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], // "CONV2D",
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[], // "RELU",
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[
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1,
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1
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] // "CONV2D",
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]
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}
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}
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