diff --git a/resnet-1470729826.pkl b/resnet-1470729826.pkl
new file mode 100644
index 0000000..a39d212
Binary files /dev/null and b/resnet-1470729826.pkl differ
diff --git a/resnet.py b/resnet.py
new file mode 100755
index 0000000..bd90b32
--- /dev/null
+++ b/resnet.py
@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+
+import keras.backend as K
+assert K.image_dim_ordering() == 'th'
+
+import pickle, time
+import sys
+import numpy as np
+from keras.callbacks import LearningRateScheduler
+from keras.datasets import mnist
+from keras.layers import BatchNormalization
+from keras.layers import Convolution2D, MaxPooling2D
+from keras.layers import Flatten, Reshape
+from keras.layers import Input, merge, Dense, Activation
+from keras.models import Model
+from keras.utils.np_utils import to_categorical
+
+nb_classes = 10
+width = 28
+height = 28
+loss='categorical_crossentropy'
+
+name = 'resnet-{:.0f}'.format(time.time())
+
+args = dict(enumerate(sys.argv))
+restore_fn = args.get(1)
+if restore_fn == '.': # TODO: accept any directory
+    # just use most recent resnet-*.pkl file in directory
+    import os
+    is_valid = lambda fn: fn.startswith('resnet-') and fn.endswith('.pkl')
+    files = sorted([fn for fn in os.listdir(restore_fn) if is_valid(fn)])
+    if len(files) == 0:
+        raise Exception("couldn't find any appropriate .pkl files in the CWD")
+    restore_fn = files[-1]
+
+dont_train = False
+verbose_summary = False
+
+reslayers = 4
+size = 8
+
+batch_size = 128
+epochs = 24
+convolutional = True
+resnet_enabled = True
+original_resnet = False
+LR = 1e-2
+LRprod = 0.1**(1/20.) # will use a tenth of the learning rate after 20 epochs
+
+use_image_generator = True
+
+def prepare(X, y):
+    X = X.reshape(X.shape[0], 1, width, height).astype('float32') / 255
+    # convert class vectors to binary class matrices
+    Y = to_categorical(y, nb_classes)
+    return X, Y
+
+# the data, shuffled and split between train and test sets
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+X_train, Y_train = prepare(X_train, y_train)
+X_test,  Y_test  = prepare(X_test,  y_test)
+
+if use_image_generator:
+    from keras.preprocessing.image import ImageDataGenerator
+    idg = ImageDataGenerator(rotation_range=5.,
+                             width_shift_range=.10,
+                             height_shift_range=.10,
+                             shear_range=5 / 180 * np.pi,
+                             zoom_range=0.1,
+                             fill_mode='constant',
+                             cval=0.)
+
+# ReLU activation is supposed to be the best with he_normal
+if convolutional:
+    layer = lambda x: Convolution2D(x, 3, 3, init='he_normal', border_mode='same')
+else:
+    layer = lambda x: Dense(x, init='he_normal')
+
+# start construting the model
+x = Input(shape=(1, width, height))
+y = x
+
+if convolutional:
+    # it might be worth trying other sizes here
+    y = Convolution2D(size, 7, 7, subsample=(2, 2), border_mode='same')(y)
+    y = MaxPooling2D()(y)
+else:
+    y = Flatten()(y)
+    y = Dense(dense_size)(y)
+
+for i in range(reslayers):
+    skip = y
+    if original_resnet:
+        y = layer(size)(y)
+        y = BatchNormalization(axis=1)(y)
+        y = Activation('relu')(y)
+        y = layer(size)(y)
+        y = BatchNormalization(axis=1)(y)
+        if resnet_enabled: y = merge([skip, y], mode='sum')
+        y = Activation('relu')(y)
+    else:
+        y = BatchNormalization(axis=1)(y)
+        y = Activation('relu')(y)
+        y = layer(size)(y)
+        y = BatchNormalization(axis=1)(y)
+        y = Activation('relu')(y)
+        y = layer(size)(y)
+        if resnet_enabled: y = merge([skip, y], mode='sum')
+
+if convolutional:
+    from keras.layers import AveragePooling1D
+    y = Reshape((size, int(width * height / 2**2 / 2**2)))(y)
+    y = AveragePooling1D(size)(y)
+    y = Flatten()(y)
+
+y = Dense(nb_classes)(y)
+y = Activation('softmax')(y)
+
+model = Model(input=x, output=y)
+
+if verbose_summary:
+    model.summary()
+else:
+    total_params = 0
+    for layer in model.layers:
+        total_params += layer.count_params()
+    print("Total params: {}".format(total_params))
+
+if restore_fn:
+    with open(restore_fn, 'rb') as f:
+        W = pickle.loads(f.read())
+    if not dont_train:
+        # sparsify an existing model
+        for i, w in enumerate(W):
+            if w.shape == (size, size, 3, 3):
+                middle = np.median(np.abs(w.flat))
+                where = np.abs(w) < middle
+                total = np.prod(w.shape)
+                fmt = 'W[{}]: zeroing {} params of {}'
+                print(fmt.format(i, int(np.count_nonzero(where)), int(total)))
+                W[i] = np.where(where, 0, w)
+    model.set_weights(W)
+    LR /= 10
+
+model.compile(loss=loss, optimizer='adam', metrics=['accuracy'])
+
+if not dont_train:
+    callbacks = [LearningRateScheduler(lambda e: LR * LRprod**e)]
+
+    kwargs = dict(
+        nb_epoch=epochs,
+        validation_data=(X_test, Y_test),
+        callbacks=callbacks,
+        verbose=1
+    )
+
+    if use_image_generator:
+        history = model.fit_generator(idg.flow(X_train, Y_train, batch_size=batch_size),
+                                      samples_per_epoch=len(X_train), **kwargs)
+    else:
+        history = model.fit(X_train, Y_train, batch_size=batch_size,
+                            **kwargs)
+
+def evaluate(X, Y):
+    score = model.evaluate(X, Y, verbose=0)
+    for name, score in zip(model.metrics_names, score):
+        if name == "acc":
+            print("{:7} {:6.2f}%".format(name, score * 100))
+        else:
+            print("{:7} {:7.5f}".format(name, score))
+
+print('TRAIN')
+evaluate(X_train, Y_train)
+
+print('TEST')
+evaluate(X_test, Y_test)
+
+print('ALL')
+evaluate(np.vstack((X_train, X_test)), np.vstack((Y_train, Y_test)))
+
+if not dont_train:
+    open(name+'.json', 'w').write(model.to_json())
+    with open(name+'.pkl', 'wb') as f:
+        f.write(pickle.dumps(model.get_weights()))