Adaptation of VGG-like convnet for custom data from http://keras.io/examples/
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import os | |
import sys | |
import json | |
import model_control | |
from numpy import loadtxt, asarray | |
from pandas import read_csv | |
from scipy.ndimage import imread | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Activation, Flatten | |
from keras.layers import Convolution2D, MaxPooling2D | |
from keras.optimizers import SGD | |
Y_train = loadtxt(model_control.y_train_file, delimiter=',', dtype = int) | |
train_files = os.listdir(model_control.train_img_path) | |
train_files = ['%s/%s' % (model_control.train_img_path, x) for x in train_files if 'jpg' in x] | |
X_train = asarray([imread(x) for x in train_files]) | |
X_train.shape #..(8144, 128, 256) (a numpy array of 8144 128x256 greyscale, i.e. single-channel, images) | |
Y_train.shape #..(8144,) (A 1-d numpy array of integer class labels) | |
model = Sequential() | |
model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(1, 128, 256))) | |
model.add(Activation('relu')) | |
model.add(Convolution2D(32, 5, 5)) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Convolution2D(64, 5, 5, border_mode='valid')) | |
model.add(Activation('relu')) | |
model.add(Convolution2D(64, 5, 5)) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Flatten()) | |
model.add(Dense(256)) | |
model.add(Activation('relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(10)) | |
model.add(Activation('softmax')) | |
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) | |
model.compile(loss='categorical_crossentropy', optimizer=sgd) | |
model.fit(X_train, Y_train, batch_size=32, nb_epoch=1, verbose=1) | |
model.save_weights('keras_net_weights.h5') | |
json_string = model.to_json() | |
with open('keta_net_structure.json', 'wb') as outfile: | |
json.dump(json_string, outfile) |
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y_train_file = '/path/to/keras_ex_data/train_labels.txt' | |
train_img_path = '/path/to/keras_ex_data' |
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