Keras unzuverlässig langsamer als TensorFlow

Question

Ich versuche, das neuronale Netzwerk von this TensorFlow example zu implementieren, aber mit Keras.

Sie finden den Code für beide Implementierungen am Ende des Posts.

Mein Problem ist, dass der Code dauert ca. 1m30 mit TensorFlow und 18 Minuten mit Keras!

Meine Frage ist:

• Habe ich TensorFlow Code bei der Übersetzung zu Keras Code ein Rookie Fehler machen?
• Oder ist Keras unglaublich langsam? Wenn ja, kann es behoben werden?

---

Tensorflow Code:

from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

import tensorflow as tf 

def weight_variable(shape): 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 

def bias_variable(shape): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 

def conv2d(x, W): 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

def max_pool_2x2(x): 
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], 
         strides=[1, 2, 2, 1], padding='SAME') 

x = tf.placeholder(tf.float32, [None, 784]) 
x_image = tf.reshape(x, [-1, 28, 28, 1]) 

y_ = tf.placeholder(tf.float32, [None, 10]) 

neurons_nb_layer_1 = 32 
neurons_nb_layer_2 = 64 
neurons_nb_layer_3 = 1024 

W_conv1 = weight_variable([5, 5, 1, neurons_nb_layer_1]) 
b_conv1 = bias_variable([neurons_nb_layer_1]) 

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

W_conv2 = weight_variable([5, 5, neurons_nb_layer_1, neurons_nb_layer_2]) 
b_conv2 = bias_variable([neurons_nb_layer_2]) 

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
h_pool2 = max_pool_2x2(h_conv2) 

W_fc1 = weight_variable([7 * 7 * neurons_nb_layer_2, neurons_nb_layer_3]) 
b_fc1 = bias_variable([neurons_nb_layer_3]) 

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * neurons_nb_layer_2]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 


keep_prob = tf.placeholder(tf.float32) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

W_fc2 = weight_variable([neurons_nb_layer_3, 10]) 
b_fc2 = bias_variable([10]) 

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) 

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

import datetime 
start = datetime.datetime.now() 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for i in range(600): 
    batch = mnist.train.next_batch(50) 
    if i % 100 == 0: 
     train_accuracy = accuracy.eval(feed_dict={ 
      x: batch[0], y_: batch[1], keep_prob: 1.0}) 
     print('step %d, training accuracy %g' % (i, train_accuracy)) 
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) 

    print('test accuracy %g' % accuracy.eval(feed_dict={ 
     x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) 
end = datetime.datetime.now() 
time = (end - start).seconds 
print(time//60, "min", time%60,"s") 

---

Keras Code:

from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

import keras 
from keras.models import Sequential 

model = Sequential() 

bias_initializer = keras.initializers.Constant(value = 0.1) 

neurons_nb_layer_1 = 32 
neurons_nb_layer_2 = 64 
neurons_nb_layer_3 = 1024 

from keras.layers import Reshape, Conv2D, MaxPooling2D, Dropout, Flatten, Dense 
model.add(Reshape((28, 28, 1), input_shape=(784,))) 
model.add(Conv2D(filters = neurons_nb_layer_1, kernel_size = 5*5, padding = 'same', activation = "relu", bias_initializer = bias_initializer)) 
model.add(MaxPooling2D(padding='same')) 
model.add(Conv2D(filters = neurons_nb_layer_2, kernel_size = 5*5, padding = 'same', activation = "relu", bias_initializer = bias_initializer)) 
model.add(MaxPooling2D(padding='same')) 
model.add(Reshape((1,7*7*neurons_nb_layer_2))) 
model.add(Dense(units = neurons_nb_layer_3, activation = "relu", bias_initializer = bias_initializer)) 
model.add(Dropout(rate = 0.5)) 
model.add(Flatten()) 
model.add(Dense(units = 10, activation = "relu")) 

model.summary() 

model.compile(loss = keras.losses.categorical_crossentropy, 
       optimizer = 'adam', 
       metrics=['accuracy'] 
      ) 


import datetime 
start2 = datetime.datetime.now() 
for i in range(600): 
    batch = mnist.train.next_batch(50) 
    if i % 100 == 0: 
     train_accuracy = model.evaluate(batch[0], batch[1]) 
     print("step", i, ":", train_accuracy) 
    model.train_on_batch(batch[0], batch[1]) 
end2 = datetime.datetime.now() 
time2 = (end2 - start2).seconds 
print(time2//60, "min", time2%60,"s") 

Answer 1

nach keras documentationkernel_size = 5*5 ist ein 25x25 convolut Ionen-Kernel, nicht 5x5 wie Ihr tensorflow Beispiel
Sie kernel_size=(5,5) oder kernel_size=5