tensorflow34《TensorFlow实战》笔记-06-02 TensorFlow实现VGGNet code

# 《TensorFlow实战》06 TensorFlow实现经典卷积神经网络# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:sz06.02.py # TensorFlow实现VGGNet# 参考 https://github.com/machrisaa/tensorflow-vggfrom datetime import datetimeimport mathimport timeimport tensorflow as tfdef conv_op(input_op, name, kh, kw, n_out, dh, dw, p):    n_in = input_op.get_shape()[-1].value    with tf.name_scope(name) as scope:        kernel = tf.get_variable(scope + "w",                                 shape = [kh, kw, n_in, n_out], dtype=tf.float32,                                 initializer = tf.contrib.layers.xavier_initializer_conv2d())        conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding='SAME')        bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32)        biases = tf.Variable(bias_init_val, trainable=True, name='b')        z = tf.nn.bias_add(conv, biases)        activation = tf.nn.relu(z, name=scope)        p += [kernel, biases]        return activationdef fc_op(input_op, name, n_out, p):    n_in = input_op.get_shape()[-1].value    with tf.name_scope(name) as scope:        kernel = tf.get_variable(scope + "w",                                 shape=[n_in, n_out], dtype=tf.float32,                                 initializer=tf.contrib.layers.xavier_initializer())        biases = tf.Variable(tf.constant(0.1, shape=[n_out], dtype=tf.float32), name='b')        activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope)        p += [kernel, biases]        return activationdef mpool_op(input_op, name, kh, kw, dh, dw):    return tf.nn.max_pool(input_op,                          ksize=[1, kh, kw, 1],                          strides=[1, dh, dw, 1],                          padding='SAME',                          name=name)def inference_op(input_op, keep_prob):    p = []    conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)    conv1_2 = conv_op(conv1_1, name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)    pool1 = mpool_op(conv1_2, name="pool1", kh=2, kw=2, dh=2, dw=2)    conv2_1 = conv_op(pool1, name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)    conv2_2 = conv_op(conv2_1, name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)    pool2 = mpool_op(conv2_2, name="pool2", kh=2, kw=2, dh=2, dw=2)    conv3_1 = conv_op(pool2, name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)    conv3_2 = conv_op(conv3_1, name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)    conv3_3 = conv_op(conv3_2, name="conv3_3", kh=3, kw=3, n_out=256, dh=1,dw=1, p=p)    pool3 = mpool_op(conv3_3, name="pool3", kh=2, kw=2, dh=2, dw=2)    conv4_1 = conv_op(pool3, name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)    conv4_2 = conv_op(conv4_1, name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)    conv4_3 = conv_op(conv4_2, name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)    pool4 = mpool_op(conv4_3, name="pool4", kh=2, kw=2, dh=2, dw=2)    conv5_1 = conv_op(pool4, name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)    conv5_2 = conv_op(conv5_1, name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)    conv5_3 = conv_op(conv5_2, name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)    pool5 = mpool_op(conv5_3, name="pool5", kh=2, kw=2, dh=2, dw=2)    shp = pool5.get_shape()    flattened_shape = shp[1].value * shp[2].value * shp[3].value    resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1")    fc6 = fc_op(resh1, name="fc6", n_out=4096, p=p)    fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop")    fc7 = fc_op(fc6_drop, name="fc7", n_out=4096, p=p)    fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")    fc8 = fc_op(fc7_drop, name="fc8", n_out=1000, p=p)    softmax = tf.nn.softmax(fc8)    predictions = tf.argmax(softmax, 1)    return predictions, softmax, fc8, pdef time_tensorflow_run(session, target, feed, info_string):    num_steps_burn_in = 10    total_duration = 0.0    total_duration_squared = 0.0    for i in range(num_batches + num_steps_burn_in):        start_time = time.time()        _ = session.run(target, feed_dict=feed)        duration = time.time() - start_time        if i >= num_steps_burn_in:            if not i % 10:                print('%s: step %d, duration = %.3f' %(datetime.now(), i - num_steps_burn_in, duration))            total_duration += duration            total_duration_squared += duration* duration    mn = total_duration / num_batches    vr = total_duration_squared / num_batches - mn * mn    sd = math.sqrt(vr)    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %          (datetime.now(), info_string, num_batches, mn, sd))def run_benchmark():    with tf.Graph().as_default():        images_size = 224        images = tf.Variable(tf.random_normal([batch_size, images_size, images_size, 3],            dtype = tf.float32,            stddev = 1e-1))        keep_prob = tf.placeholder(tf.float32)        predictions, softmax, fc8, p = inference_op(images, keep_prob)        init = tf.global_variables_initializer()        sess = tf.Session()        sess.run(init)        time_tensorflow_run(sess, predictions, {keep_prob: 1.0}, "Forward")        objective = tf.nn.l2_loss(fc8)        grad = tf.gradients(objective, p)        time_tensorflow_run(sess, grad, {keep_prob: 0.5}, "Forward-backward")batch_size = 32num_batches = 100run_benchmark()'''2017-04-13 20:46:38.301421: step 0, duration = 0.8762017-04-13 20:46:47.062714: step 10, duration = 0.8762017-04-13 20:46:55.825010: step 20, duration = 0.8762017-04-13 20:47:04.586824: step 30, duration = 0.8762017-04-13 20:47:13.350110: step 40, duration = 0.8762017-04-13 20:47:22.112488: step 50, duration = 0.8762017-04-13 20:47:30.879796: step 60, duration = 0.8762017-04-13 20:47:39.645100: step 70, duration = 0.8772017-04-13 20:47:48.409402: step 80, duration = 0.8762017-04-13 20:47:57.172700: step 90, duration = 0.8762017-04-13 20:48:05.061676: Forward across 100 steps, 0.876 +/- 0.000 sec / batch2017-04-13 20:48:48.393882: step 0, duration = 3.4342017-04-13 20:49:22.706495: step 10, duration = 3.4122017-04-13 20:49:57.018747: step 20, duration = 3.4322017-04-13 20:50:31.359640: step 30, duration = 3.4272017-04-13 20:51:05.571630: step 40, duration = 3.4192017-04-13 20:51:39.908891: step 50, duration = 3.4592017-04-13 20:52:14.316013: step 60, duration = 3.4232017-04-13 20:52:48.706655: step 70, duration = 3.4282017-04-13 20:53:23.171285: step 80, duration = 3.4552017-04-13 20:53:57.729194: step 90, duration = 3.4602017-04-13 20:54:28.629420: Forward-backward across 100 steps, 3.437 +/- 0.020 sec / batch'''