Added support for json load of params in train

Updated gitignore

.gitignore

@@ -112,3 +112,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# Others
+testimage.png

loulou.py

@@ -0,0 +1,2 @@
+import scripts.train as train
+import scripts.run as run

scripts/ffl.py

@@ -18,7 +18,7 @@ def grads(X, Y, weights):
         grads[i-1] = a[i-1].T.dot(delta) # calculating errors of weights and storing onto |grads|
     return grads / len(X)
 
-def train(weights, trX, trY, teX, teY, filename=False, epochs=30, batch=20, learning_rate=0.03):
+def train(weights, trX, trY, teX, teY, filename, epochs, batch, learning_rate):
     prediction = np.argmax(feed_forward(teX, weights)[-1], axis=1)
     print(0, np.mean(prediction == np.argmax(teY, axis=1)))
     for i in range(epochs):

scripts/train.py

@@ -1,9 +1,23 @@
 import mnist
 from ffl import *
+import json
 
-if __name__ == '__main__':
-    filename = 'four layers.npy'
-    filename = '../trains/' + filename
+def runTrain(params, file='trained.npy'):
+    params = json.loads(params)
+    epochs = params['epochs']
+    batch = params['batch']
+    learning_rate = params['learning_rate']
+    file = '../trains/' + file
     trX, trY, teX, teY = mnist.load_data()
     weights = [np.random.randn(*w) * 0.1 for w in [(784, 400), (400,200), (200,100), (100, 10)]]
-    train(weights, trX, trY, teX, teY, filename=filename, epochs=0)
+    train(weights, trX, trY, teX, teY, 'trained.npy', epochs, batch, learning_rate)
+
+if __name__ == '__main__':
+    params = {}
+    params['epochs'] = 30
+    params['batch'] = 20
+    params['learning_rate'] = 0.03
+    params = json.dumps(params)
+
+    filename = 'trained.npy'
+    runTrain(params, file=filename)