import tensorflow as tf import tensorflow.keras as keras from tensorflow.keras import layers from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Dropout, Flatten, MaxPooling2D, Conv2D from tensorflow.keras.models import Model, Sequential from tensorflow.keras.datasets import mnist from tensorflow.keras.utils import plot_model, to_categorical import numpy as np from IPython import embed import sys sys.path.append('..') from hostLib.layers.conv2D import Conv2D as Conv2DFPGA batch_size = 128 num_classes = 10 epochs = 1200 # input image dimensions img_rows, img_cols = 28, 28 # the data, 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], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) x_train = x_train.astype('float') x_test = x_test.astype('float') #x_train /= 255 #x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = to_categorical(y_train, num_classes) y_test = to_categorical(y_test, num_classes) a = layers.Input(dtype=tf.int32, shape=(28, 28, 1)) b = Conv2DFPGA(2)(a) c = Conv2DFPGA(1)(a) d = Conv2DFPGA(1)(b) e = Conv2DFPGA(2)(c) print(a) print(b) print(c) print(d) print(e) x = layers.Add()([d,e]) y = layers.Flatten()(x) z = layers.Dense(num_classes, activation='softmax')(y) model = Model(inputs=a, outputs=z) print(model.output_shape) """ model = Sequential() model.add(Conv2DFPGA([0,0])) model.add(Conv2DFPGA([0,0])) model.add(Conv2DFPGA([0,0])) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) """ model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) plot_model(model, to_file='model.png', expand_nested=True, show_shapes=True) 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])