First commit. Accuracy 22%

Author: goelakash

Mona Lisa.jpg

@@ -0,0 +1,61 @@
+# import theano
+# import theano.tensor as T
+import numpy as np
+import cv2
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation
+from keras.optimizers import SGD
+from random import randint
+
+image_file = cv2.imread("Mona Lisa.jpg")
+
+x1 = []
+x2 = []
+y1 = []
+y2 = []
+
+for i in xrange(image_file.shape[0]):
+    for j in xrange(image_file.shape[1]):
+        prob = randint(0,9)
+        if prob>0:
+            # np.insert(X_train,[[i,j]], axis=0)
+            # np.insert(Y_train,[image_file[i,j,:].astype('float32')/255], axis=0)
+
+            x1.append([i,j])
+            y1.append(image_file[i,j,:].astype('float32')/255)
+        else:
+            # np.insert(X_test,[[i,j]], axis=0)
+            # np.insert(Y_test,[image_file[i,j,:].astype('float32')/255], axis=0)
+
+            x2.append([i,j])
+            y2.append(image_file[i,j,:].astype('float32')/255)
+
+
+X_train = np.array(x1)
+X_test = np.array(x2)
+Y_train = np.array(y1)
+Y_test = np.array(y2)
+
+print len(X_train), len(Y_train), len(X_test), len(Y_test)
+print X_train[:5], Y_train[:5]
+
+model = Sequential()
+model.add(Dense(100, input_dim=2, init='uniform'))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+# model.add(Dense(100, init='uniform'))
+# model.add(Activation('relu'))
+# model.add(Dropout(0.5))
+model.add(Dense(3, init='uniform'))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+
+sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='mean_squared_error', optimizer=sgd)
+
+model.fit(X_train, Y_train,
+          nb_epoch=20,
+          batch_size=20,
+          show_accuracy=True)
+
+score = model.evaluate(X_test, Y_test, batch_size=20)