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私はtensorflowを使ってgoogleから提供されたSVHNデータセットを使用して数字を分類できるモデルを作成しています。マイクレートは実際には低いです（〜25％）が、正確さ。 Reference NotebookTensorflowの予測精度を上げる方法
私のモデルをより良くするために、正確さをどのように向上させるべきか、誰かに私にいくつかのヒントを教えてもらえないかと思っていました。
ここは私のモデルコードです。
filename='extra.pickle'
with open(filename,'rb') as f:
other=pickle.load(f)
train_data=other['train_dataset']
test_data=other['test_dataset']
del other
train_dataset=train_data['X']
test_dataset=test_data['X']
train_labels=train_data['y']
test_labels=test_data['y']
print(len(test_dataset))
print(len(test_labels))
print(test_dataset.shape)
#print(train_dataset.length())
classes=10
batch_size=32
num_steps = 200000
graph=tf.Graph()
#Placeholder for the data
with graph.as_default():
data_placeholder = tf.placeholder(tf.float32, shape=(batch_size,32,32,3))
label_placeolder = tf.placeholder(tf.int64, shape=(batch_size, classes))
tf_test_dataset = tf.placeholder(tf.float32, shape=(batch_size,32,32,3))
tf_label_dataset = tf.placeholder(tf.float32, shape=(batch_size, classes))
layer1_weights=tf.Variable(tf.truncated_normal([3,3,3,16]))
layer1_biases=tf.Variable(tf.zeros([16]))
layer2_weights=tf.Variable(tf.truncated_normal([3,3,16,32]))
layer2_biases=tf.Variable(tf.zeros([32]))
layer3_weights=tf.Variable(tf.truncated_normal([2,2,32,64]))
layer3_biases=tf.Variable(tf.zeros([64]))
layer4_weights=tf.Variable(tf.truncated_normal([1024,10]))
layer4_biases=tf.Variable(tf.zeros([10]))
layer5_weights=tf.Variable(tf.truncated_normal([10,classes]))
layer5_biases=tf.Variable(tf.zeros([classes]))
def layer_multiplication(data_input_given,dropping=False):
#Convolutional Layer 1
CNN1=tf.nn.relu(tf.nn.conv2d(data_input_given,layer1_weights,strides=[1,1,1,1],padding='SAME')+layer1_biases)
print('CNN1 Done!!')
#Pooling Layer
Pool1=tf.nn.max_pool(CNN1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
print('Pool1 DOne')
#second Convolution layer
CNN2=tf.nn.relu(tf.nn.conv2d(Pool1,layer2_weights,strides=[1,1,1,1],padding='SAME'))+layer2_biases
print('CNN2 Done')
#Second Pooling
Pool2 = tf.nn.max_pool(CNN2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
print('pool2 Done')
#Third Convolutional Layer
print(Pool2.shape)
CNN3 = tf.nn.relu(tf.nn.conv2d(Pool2, layer3_weights, strides=[1, 1, 1, 1], padding='SAME')) + layer3_biases
print('CNN3 Done')
#Third Pooling Layer
Pool3 = tf.nn.max_pool(CNN3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
print('Pool3 DOne')
#Fully Connected Layer
#print(Pool3.shape)
shape = Pool3.get_shape().as_list()
# print(shape)
reshape = tf.reshape(Pool3, [shape[0], shape[1] * shape[2] * shape[3]])
#print(reshape.shape)
FullyCon = tf.nn.relu(tf.matmul(reshape, layer4_weights) + layer4_biases)
#print(FullyCon.shape)
if dropping==False:
print('Training')
dropout = tf.nn.dropout(FullyCon, 0.6)
z=tf.matmul(dropout,layer5_weights)+layer5_biases
return z
else:
print('Testing')
z = tf.matmul(FullyCon, layer5_weights) + layer5_biases
return z
gloabl_step = tf.Variable(0, trainable=False)
decay_rate=tf.train.exponential_decay(1e-6,gloabl_step,4000,0.96,staircase=False,)
train_input=layer_multiplication(data_placeholder,False)
test_prediction = tf.nn.softmax(layer_multiplication(tf_test_dataset,True))
loss=(tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label_placeolder,logits=train_input))
+ 0.01 * tf.nn.l2_loss(layer1_weights)
+ 0.01 * tf.nn.l2_loss(layer2_weights)
+ 0.01 * tf.nn.l2_loss(layer3_weights)
+ 0.01 * tf.nn.l2_loss(layer4_weights)
+ 0.01 * tf.nn.l2_loss(layer5_weights)
)
optimizer = tf.train.GradientDescentOptimizer(name='Stochastic', learning_rate=decay_rate).minimize(loss,global_step=gloabl_step)
def accuracy(predictions, labels):
print(predictions.shape[0])
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/predictions.shape[0])
config=tf.ConfigProto()
config.gpu_options.allocator_type ='BFC'
saver = tf.train.Saver()
test_accuracy=[]
with tf.Session(config=config) as session:
tf.global_variables_initializer().run()
print('Initialized')
tf.train.write_graph(session.graph_def, '.', './SVHN.pbtxt')
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
batch_data = train_dataset[offset:(offset + batch_size), :, :]
batch_labels = train_labels[offset:(offset + batch_size), :]
batch_test_data = test_dataset[offset:(offset + batch_size), :, :]
batch_test_labels = test_labels[offset:(offset + batch_size),:]
#print(batch_data)
#print(batch_test.shape)
feed_dict = {data_placeholder:batch_data, label_placeolder:batch_labels}
_, l, predictions = session.run(
[optimizer, loss, train_input], feed_dict=feed_dict)
if (step % 500 == 0):
#print(session.run(decay_rate))
print('Minibatch loss at step %d: %f' % (step, l))
print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))
if(batch_test_data.shape!=(32,32,32,3)):
print('Skip')
else:
correct_prediction = tf.equal(tf.argmax(test_prediction, 1), tf.argmax(tf_label_dataset, 1))
accuracy_for_test = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Test Accuracy")
test_accuracy.append(accuracy_for_test.eval(feed_dict={tf_test_dataset:batch_test_data,
tf_label_dataset:batch_test_labels}))
print(accuracy_for_test.eval(feed_dict={tf_test_dataset:batch_test_data,
tf_label_dataset:batch_test_labels}))
print(np.mean(test_accuracy))
saver.save(sess=session, save_path='./SVHN.ckpt')
Ps-コードは私のシステムで動作します。この問題は私の考えであると思われます。