From d2f7ba7701f469e96274fb9d5886c1462ec66c95 Mon Sep 17 00:00:00 2001 From: Anshuman Chhabra Date: Sun, 15 Jul 2018 15:11:31 +0530 Subject: [PATCH] Added Keras example --- examples/keras-example/mnist_keras.py | 73 ++++++++++++++++++++++++++ examples/keras-example/mnist_mlp.pb | Bin 0 -> 5364414 bytes 2 files changed, 73 insertions(+) create mode 100644 examples/keras-example/mnist_keras.py create mode 100644 examples/keras-example/mnist_mlp.pb diff --git a/examples/keras-example/mnist_keras.py b/examples/keras-example/mnist_keras.py new file mode 100644 index 0000000..898ef37 --- /dev/null +++ b/examples/keras-example/mnist_keras.py @@ -0,0 +1,73 @@ +'''Trains a simple deep NN on the MNIST dataset. +Gets to 98.40% test accuracy after 20 epochs +(there is *a lot* of margin for parameter tuning). +2 seconds per epoch on a K520 GPU. +''' + +from __future__ import print_function +import keras +from keras.datasets import mnist +from keras.models import Sequential +from keras.layers import Dense, Dropout +from keras.optimizers import RMSprop +from tensorflow.python.framework.graph_util import convert_variables_to_constants +from keras import backend +import tensorflow as tf + +def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True): + graph = session.graph + with graph.as_default(): + freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or [])) + output_names = output_names or [] + output_names += [v.op.name for v in tf.global_variables()] + input_graph_def = graph.as_graph_def() + if clear_devices: + for node in input_graph_def.node: + node.device = "" + frozen_graph = convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names) + return frozen_graph + +batch_size = 128 +num_classes = 10 +epochs = 20 + +# the data, split between train and test sets +(x_train, y_train), (x_test, y_test) = mnist.load_data() + +x_train = x_train.reshape(60000, 784) +x_test = x_test.reshape(10000, 784) +x_train = x_train.astype('float32') +x_test = x_test.astype('float32') +x_train /= 255 +x_test /= 255 +print(x_train.shape[0], 'train samples') +print(x_test.shape[0], 'test samples') + +# convert class vectors to binary class matrices +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) + +model = Sequential() +model.add(Dense(512, activation='relu', input_shape=(784,), name="input")) +model.add(Dropout(0.2)) +model.add(Dense(512, activation='relu')) +model.add(Dropout(0.2)) +model.add(Dense(num_classes, activation='softmax', name="output")) + +model.summary() + +model.compile(loss='categorical_crossentropy', + optimizer=RMSprop(), + metrics=['accuracy']) + +history = 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]) + +frozen_graph = freeze_session(backend.get_session(), output_names=[out.op.name for out in model.outputs]) +tf.train.write_graph(frozen_graph, "./", "mnist_mlp.pb", as_text=False) diff --git a/examples/keras-example/mnist_mlp.pb b/examples/keras-example/mnist_mlp.pb new file mode 100644 index 0000000000000000000000000000000000000000..cb87c9be64e2a9789d0c515311c0976e1dfdeef4 GIT binary patch literal 5364414 zcmY(qe^iX?_y0eVB*{pUL`hN!gQVu(J0nSwBuP?9QmK$6sbu>7Lr6jhgOHym3F*Ez zBOxS72%(dZgb*j4Ue5Ray#M?DGi%LS&8*fv*WTB2Kc3I6VWrW3)tZP6QOo}CRlC1i z`0^DiL)L}|u8g$kuhAzkYE#5YZ52tc|MzBn$nyX9Mk>;{BM;Ef{J&333|kqwW@Wf` zpP6gdtdIKtf8>bpSAC+Fhi_P^y+cv-j|EF1Be|x>DRQ`*rlbJ1K}w{Q7y?6Jx=X_h%{HMiRok>dbTKgyGyFP@R_xa z(`Fq8(=q#06HMq6N|#q5>6G3EdGIA>e||Bh=B82cZ#~vCCxx7|-!r%5i75Z9&Nr?0 z=Zf}BB^|S$EMxP3Ad9If4;(*)Y1M_2Tl4^wzR6`8B?cJtqMdn-sDRe`71-I`1rBBE z*fZ9PPOlw}qRE+8)y}+P_TdtIeSR((T*{>C+wmw`PF(3NT{Jjx8Enn1NJsvMweFn? z9irisQ@shp&6lu?6ifF_GicAuDcEa9Fpa7kLWQ5Fvb_nLsmg66Yr4LIH+YyyugBO@ 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