added mnist fc

Author: omaraflak

example_mnist.py renamed to example_mnist_conv.py

@@ -0,0 +1,50 @@
+import numpy as np
+
+from network import Network
+from fc_layer import FCLayer
+from activation_layer import ActivationLayer
+from activations import tanh, tanh_prime
+from losses import mse, mse_prime
+
+from keras.datasets import mnist
+from keras.utils import np_utils
+
+# load MNIST from server
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# training data : 60000 samples
+# reshape and normalize input data
+x_train = x_train.reshape(x_train.shape[0], 1, 28*28)
+x_train = x_train.astype('float32')
+x_train /= 255
+# encode output which is a number in range [0,9] into a vector of size 10
+# e.g. number 3 will become [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
+y_train = np_utils.to_categorical(y_train)
+
+# same for test data : 10000 samples
+x_test = x_test.reshape(x_test.shape[0], 1, 28*28)
+x_test = x_test.astype('float32')
+x_test /= 255
+y_test = np_utils.to_categorical(y_test)
+
+# Network
+net = Network()
+net.add(FCLayer(28*28, 100))                # input_shape=(1, 28*28)    ;   output_shape=(1, 100)
+net.add(ActivationLayer(tanh, tanh_prime))
+net.add(FCLayer(100, 50))                   # input_shape=(1, 100)      ;   output_shape=(1, 50)
+net.add(ActivationLayer(tanh, tanh_prime))
+net.add(FCLayer(50, 10))                    # input_shape=(1, 50)       ;   output_shape=(1, 10)
+net.add(ActivationLayer(tanh, tanh_prime))
+
+# train on 1000 samples
+# as we didn't implemented mini-batch GD, training will be pretty slow if we update at each iteration on 60000 samples...
+net.use(mse, mse_prime)
+net.fit(x_train[0:1000], y_train[0:1000], epochs=35, learning_rate=0.1)
+
+# test on 3 samples
+out = net.predict(x_test[0:3])
+print("\n")
+print("predicted values : ")
+print(out, end="\n")
+print("true values : ")
+print(y_test[0:3])