我無法理解尺寸部分。它是關於我設置的形狀[1,15]嗎?尺寸必須相同,但對於'MatMul_1'(op:'MatMul'),其值爲15和1,輸入形狀爲:[1,15],[1,500]
import tensorflow as tf
import numpy as np
import pandas as pd
with open('train.CSV', 'r') as f:
data0 = f.readlines()
for line in data0:
odom = line.split()
numbers_float0 = map(float, odom)
with open('trainY.CSV', 'r') as f:
data1 = f.readlines()
for line in data1:
odom = line.split()
numbers_float1 = map(float, odom)
with open('test.CSV', 'r') as f:
data2 = f.readlines()
for line in data2:
odom = line.split()
numbers_float2 = map(float, odom)
with open('Test Y.CSV', 'r') as f:
data3 = f.readlines()
for line in data3:
odom = line.split()
numbers_float3 = map(float, odom)
train_x,train_y,test_x,test_y = ('numbers_float0','numbers_float1','numbers_float2','numbers_float3')
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 2
batch_size = 100
hm_epochs = 10
x =tf.placeholder('float',[1,15])
y = tf.placeholder('float',[1,1])
hidden_1_layer = {'f_fum':n_nodes_hl1,
'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'f_fum':n_nodes_hl2,
'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_layer = {'f_fum':n_nodes_hl3,
'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}
output_layer = {'f_fum':None,
'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
'bias':tf.Variable(tf.random_normal([n_classes])),}
# Nothing changes
def neural_network_model(data):
l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
l2 = tf.nn.relu(l2)
l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
l3 = tf.nn.relu(l3)
output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']
return output
def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
#tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss = 0
i=0
while i < len(train_x):
start = i
end = i+batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
y: batch_y})
epoch_loss += c
i+=batch_size
print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
train_neural_network(x)
這裏是跟蹤誤差:enter image description here
下面是數據train.CSV enter image description here
我使用的Y數據只有一列。
由於OP需要學習矩陣乘法的工作原理 –
當我將節點數改爲15時,跟蹤誤差變爲維0,這兩個形狀必須相等,但是'SoftmaxCrossEntropyWithLogits_7'(op:'SoftmaxCrossEntropyWithLogits')的輸入形狀爲[15,1],[0,1]。這是否意味着成本函數不適合我的數據? –