在这个简单的NN示例中,为什么Tensorflow比convnetjs慢100倍？

我一直在使用convnetjs 1年,现在我想继续使用更强大,更快速的库.我认为Tensorflow比JS库快几个数量级,所以我为两个库编写了一个简单的神经网络,并进行了一些测试.它是一个3-5-5-1神经网络,在一个单一的例子中训练了一定数量的具有SGD和RELU层的时期.

Tensorflow代码:

import tensorflow as tf
import numpy
import time

NUM_CORES = 1  # Choose how many cores to use.
sess = tf.Session(config=tf.ConfigProto(inter_op_parallelism_threads=NUM_CORES, intra_op_parallelism_threads=NUM_CORES))

# Parameters
learning_rate = 0.001
training_epochs = 1000
batch_size = 1

# Network Parameters
n_input = 3 # Data input
n_hidden_1 = 5 # 1st layer num features
n_hidden_2 = 5 # 2nd layer num features
n_output = 1 # Data output

# tf Graph input
x = tf.placeholder("float", [None, n_input], "a")
y = tf.placeholder("float", [None, n_output], "b")

# Create model
def multilayer_perceptron(_X, _weights, _biases):
    layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) #Hidden layer with RELU activation
    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) #Hidden layer with RELU activation
    return tf.matmul(layer_2, _weights['out']) + _biases['out']

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_output]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_output]))
}

# Construct model
pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer
cost = tf.reduce_sum(tf.nn.l2_loss(pred-y)) / batch_size # L2 loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
sess.run(init)

# Training Data
train_X = numpy.asarray([[0.1,0.2,0.3]])
train_Y = numpy.asarray([[0.5]])

# Training cycle
start = time.clock()
for epoch in range(training_epochs):
    # Fit training using batch data
    sess.run(optimizer, feed_dict={x: train_X, y: train_Y})
end = time.clock()

print end - start #2.5 seconds -> 400 epochs per second 
print "Optimization Finished!"

    
    
    
    
    


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