commit a69f7eb86e9dbc6811690ba4fdb515842b9d678e
parent f1eac97a35c5046541dfce774449dcbabc66abdf
Author: David Freifeld <freifeld.david@gmail.com>
Date: Fri, 10 Jul 2020 16:16:12 -0700
Attempts to use wandb sweep
Diffstat:
| M | example.py | | | 47 | ++++++++++++++++++++--------------------------- |
| A | sweep.yaml | | | 36 | ++++++++++++++++++++++++++++++++++++ |
2 files changed, 56 insertions(+), 27 deletions(-)
diff --git a/example.py b/example.py
@@ -1,3 +1,6 @@
+import sys
+sys.path.append("/Users/davidfreifeld/projects/Jacobian/")
+print(sys.path)
import mrbpnn
import numpy as np
import matplotlib.pyplot as plt
@@ -5,39 +8,29 @@ import numpy
import time
import wandb
-batch_sz = 10
-layers = 1
-epochs = 50
-lr = 0.0155
-bias_lr = 0.03
-neurons = 10
-l = 0.5
-ratio = 0.9
-layers_list = []
+data_split = 0.75
+
+hyperparameter_defaults = dict(batch_size = 10,
+ hidden_layers = 1,
+ epochs = 50,
+ learning_rate = 0.0155,
+ bias_lr = 0.03,
+ activation = "lecun_tanh",
+ neurons = 10,
+ l = 0.5)
+
+wandb.init(project="jacobian", config=hyperparameter_defaults)
+config = wandb.config
init = time.time()
-net = mrbpnn.Network("./data_banknote_authentication.txt", batch_sz, lr, bias_lr, l, ratio)
+net = mrbpnn.Network("./data_banknote_authentication.txt", config.batch_size, config.learning_rate, config.bias_lr, config.l, data_split)
net.add_layer(4, "linear")
-layers_list.append(4)
-for i in range(layers):
- net.add_layer(neurons, "lecun_tanh")
- layers_list.append(neurons)
+for i in range(config.hidden_layers):
+ net.add_layer(config.neurons, config.activation)
net.add_layer(1, "resig")
-layers_list.append(1)
net.initialize()
-wandb.init(project="jacobian")
-wandb.config.update({"epochs": epochs,
- "batch_size": batch_sz,
- "learning_rate": lr,
- "bias_lr": bias_lr,
- "hidden_layers": layers,
- "activation":"lecun_tanh",
- "neurons": neurons,
- "lambda" : l,
- "data_split":ratio})
-
-for i in range(epochs):
+for i in range(config.epochs):
net.train()
wandb.log({'accuracy': net.get_acc(), 'cost': net.get_cost(), 'val_accuracy': net.get_val_acc(), 'val_cost': net.get_val_cost()})
end = time.time()
diff --git a/sweep.yaml b/sweep.yaml
@@ -0,0 +1,36 @@
+program: example.py
+method: bayes
+metric:
+ name: val_cost
+ goal: minimize
+parameters:
+ epochs:
+ distribution: int_uniform
+ min: 25
+ max: 100
+ batch_size:
+ distribution: int_uniform
+ min: 5
+ max: 200
+ hidden_layers:
+ distribution: int_uniform
+ min: 1
+ max: 6
+ bias_lr:
+ distribution: uniform
+ min: 0.0015
+ max: 0.06
+ neurons:
+ distribution: int_uniform
+ min: 2
+ max: 40
+ activation:
+ distribution: categorical
+ values:
+ - lecun_tanh
+ - relu
+ - sigmoid
+ learning_rate:
+ distribution: uniform
+ min: 0.001
+ max: 0.5
