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+# from https://keras.io/examples/mnist_cnn/
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+
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+import tensorflow as tf
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+import tensorflow.keras as keras
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+from tensorflow.keras import layers
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+from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Dropout, Flatten, MaxPooling2D, Conv2D
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+from tensorflow.keras.models import Model, Sequential
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+from tensorflow.keras.datasets import mnist
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+from tensorflow.keras.utils import plot_model, to_categorical
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+
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+import numpy as np
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+from IPython import embed
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+
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+import sys
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+sys.path.append('../hostLib/')
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+from hostLib.layers.conv2d import Conv2D as Conv2DFPGA
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+
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+batch_size = 128
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+num_classes = 10
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+epochs = 1200
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+
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+# input image dimensions
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+img_rows, img_cols = 28, 28
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+
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+# the data, split between train and test sets
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+(x_train, y_train), (x_test, y_test) = mnist.load_data()
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+
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+x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
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+x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
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+
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+x_train = x_train.astype('float')
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+x_test = x_test.astype('float')
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+#x_train /= 255
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+#x_test /= 255
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+print('x_train shape:', x_train.shape)
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+print(x_train.shape[0], 'train samples')
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+print(x_test.shape[0], 'test samples')
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+
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+# convert class vectors to binary class matrices
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+y_train = to_categorical(y_train, num_classes)
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+y_test = to_categorical(y_test, num_classes)
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+
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+model = Sequential()
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+model.add(layers.Lambda(lambda x: tf.image.resize(x, (228,228)))) #to-do: implement 2 stage 28x28_3x3 conv2d with relu
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+model.add(Conv2DFPGA(1))#32
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+model.add(layers.Activation('relu'))
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+model.add(layers.Lambda(lambda x: tf.image.resize(x, (228,228))))
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+model.add(Conv2DFPGA(1))#64
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+model.add(layers.Activation('relu'))
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+model.add(MaxPooling2D(pool_size=(int(228/28*2), int(228/28*2))))
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+model.add(Dropout(0.25))
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+model.add(Flatten())
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+model.add(Dense(128, activation='relu'))
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+model.add(Dropout(0.5))
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+model.add(Dense(num_classes, activation='softmax'))
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+
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+model.compile(loss=keras.losses.categorical_crossentropy,
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+ optimizer=keras.optimizers.Adadelta(),
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+ metrics=['accuracy'])
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+
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+plot_model(model, to_file='model.png', expand_nested=True, show_shapes=True)
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+
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+model.fit(x_train, y_train,
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+ batch_size=batch_size,
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+ epochs=epochs,
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+ verbose=1,
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+ validation_data=(x_test, y_test))
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+
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+score = model.evaluate(x_test, y_test, verbose=0)
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+print('Test loss:', score[0])
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+print('Test accuracy:', score[1])
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+
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