Package: simnets
# simnets-tf## SimNets implementation in TensorFlow### Binary installationBinary installation requires a cuda toolkit installation >= 7.5. <BR/>Download the .whl file from the GitHub release tab, then type:```python -m pip install <whl file>```all requirements should be installed automatically.### Building from SourceBuilding from source requires:1. A working c++ compiler with c++11 support (gcc >= 4.7)2. Cuda toolkit installed (for nvcc)3. CMake >= 3.0 (<code>apt install cmake</code>)4. TensorFlow installed for the Python interpreter you intend to use<B>Important:</B> The following command should run without error:```python -c 'import tensorflow as tf'```To build the project type the following commands:<BR/>Python 2.7:<BR/>```git clone --recursive https://github.com/HUJI-Deep/simnets-tf.gitcd simnets-tfmkdir buildcd buildcmake .. -DCMAKE_BUILD_TYPE=Release -DSIMNETS_PYTHON_VERSION=2.7 -DCMAKE_INSTALL_PREFIX=installmake -j simnet_ops```Python 3.5:<BR/>```git clone --recursive https://github.com/HUJI-Deep/simnets-tf.gitcd simnets-tfmkdir buildcd buildcmake .. -DCMAKE_BUILD_TYPE=Release -DSIMNETS_PYTHON_VERSION=3.5 -DCMAKE_INSTALL_PREFIX=installmake -j simnet_ops```To test the code you can now type:```make test_simnet_ops```This should run for about two minutes and return without any errors.<BR/>Now you can create a .whl file:```make create_wheel```Finally, to install the simnets-tf package type (remember to use the right interpreter):```cd install/distpython -m pip install <whl file>```The installation is successful if the following runs (again, remember to use the right interpreter):```python -c 'import simnets'```### Usage example#### Keras```pythonimport simnets.keras as skimport kerasfrom keras.datasets import mnistfrom keras.models import Modelfrom keras.layers import Input, Flatten, AveragePooling2D, Lambdafrom keras import backend as Kimport numpy as npbatch_size = 64num_classes = 10sim_kernel = 2sim_channels = 32mex_channels = sim_channelsepochs = 3# input image dimensionsimg_rows, img_cols = 28, 28# the data, shuffled and split between train and test sets(x_train, y_train), (x_test, y_test) = mnist.load_data()x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)input_shape = (1, img_rows, img_cols)x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_train = x_train / 255.0 - 0.5x_test = x_test / 255.0 - 0.5print(x_train.shape[0], 'train samples')print(x_test.shape[0], 'test samples')# convert class vectors to binary class matricesy_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)def sum_pooling_layer(x, pool_size):x = AveragePooling2D(pool_size=pool_size, padding='valid')(x)x = Lambda(lambda x: x * pool_size[0] * pool_size[1])(x)return xa = Input(shape=(1, img_rows, img_cols))b = sk.Similarity(sim_channels,ksize=[2, 2], strides=[2, 2], similarity_function='L2',normalization_term=True, padding=[2, 2], out_of_bounds_value=np.nan, ignore_nan_input=True)(a)while b.shape[-2:] != (1, 1):mex_channels *= 2b = sk.Mex(mex_channels,blocks=[int(b.shape[-3]), 1, 1], strides=[int(b.shape[-3]), 1, 1],softmax_mode=True, normalize_offsets=True,use_unshared_regions=True, unshared_offset_region=[2])(b)b = sum_pooling_layer(b, pool_size=(2, 2))b = sk.Mex(num_classes,blocks=[mex_channels, 1, 1], strides=[mex_channels, 1, 1],softmax_mode=True, normalize_offsets=True,use_unshared_regions=True, shared_offset_region=[1])(b)b = Flatten()(b)model = Model(inputs=[a], outputs=[b])print(model.summary())def softmax_loss(y_true, y_pred):return K.categorical_crossentropy(y_pred, y_true, True)model.compile(loss=softmax_loss,optimizer=keras.optimizers.nadam(lr=1e-2, epsilon=1e-6),metrics=['accuracy'])sk.perform_unsupervised_init(model, 'kmeans', layers=None, data=x_train, batch_size=100)model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(x_test, y_test))score = model.evaluate(x_test, y_test, verbose=0)print('Test loss:', score[0])print('Test accuracy:', score[1])```#### Low level```pythonimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datafrom simnets import similarityfrom simnets.unsupervised import similarity_unsupervised_initimport matplotlib.pyplot as pltmnist = input_data.read_data_sets('MNIST_data', one_hot=True)sess = tf.InteractiveSession()x = tf.placeholder(tf.float32, shape=[None, 784])xr = tf.reshape(x, [-1, 1, 28, 28])y_ = tf.placeholder(tf.float32, shape=[None, 10])shape = [10, 1, 28, 28]templates = tf.Variable(tf.truncated_normal(shape, stddev=0.1))weights_var = tf.Variable(tf.truncated_normal(shape, stddev=0.1))weights = tf.abs(weights_var)sim = similarity(xr, templates, weights, similarity_function='L2', ksize=[28,28], strides=[28,28], padding=[0,0])y = tf.reshape(sim, [-1, 10])kmo_init, kmo = similarity_unsupervised_init('kmeans', sim, templates, weights_var)cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)sess.run(tf.global_variables_initializer())for idx in range(300):batch = mnist.train.next_batch(100)if idx == 0:kmo_init.run(feed_dict={x: batch[0], y_: batch[1]})kmo.run(feed_dict={x: batch[0], y_: batch[1]})if (idx + 1) % 100 == 0:print('kmeans', idx+1, '/', 1000)def normalize(img):return (img - img.min()) / (img.max() - img.min())templates_np = tf.get_default_session().run(templates)plt.figure(1)for i in range(10):plt.subplot(4,3, i+1)plt.imshow(normalize(templates_np[i,0,...]))plt.show()for idx in range(1000):batch = mnist.train.next_batch(100)train_step.run(feed_dict={x: batch[0], y_: batch[1]})if (idx + 1) % 100 == 0:print(idx+1, '/', 1000)correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))print('Accuracy:', accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))```