テンソルフロー - nn =>正確なプリントエラー

Question

テンソルフローを使用してnnを使用します。

多入力=>線形回帰。私は正確にtensorflow例はないよ

は...

はちょうど私は、この例だけでチェックするのbecuase成功をwannna。

（入力されたデータは、フルーツ&水&野菜
出力値が実数（濃度）だから、

です、私はこの例では似ていると思います。

あなたはもっと良い例を持っている場合は、私に与えてください..あなたに感謝。

このソース印刷精度ならば、これはエラーを持っている。

from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 

import tensorflow as tf 

from tensorflow.contrib import learn 

from sklearn.model_selection import train_test_split 

boston = learn.datasets.load_dataset('boston') 
x, y = boston.data, boston.target 
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.6, random_state=42) 

total_len = X_train.shape[0] 

# Parameters 
learning_rate = 0.001 
training_epochs = 500 
batch_size = 10 
display_step = 1 
dropout_rate = 0.9 

# Network Parameters 
n_hidden_1 = 32 # 1st layer number of features 
n_hidden_2 = 200 # 2nd layer number of features 
n_hidden_3 = 200 
n_hidden_4 = 256 
n_input = X_train.shape[1] 
n_classes = 1 

# tf Graph input기 
x = tf.placeholder("float", [None,13]) 
y = tf.placeholder("float", [None]) 

# Create model 
def multilayer_perceptron(x, weights, biases): 
    # Hidden layer with RELU activation 
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) 
    layer_1 = tf.nn.relu(layer_1) 

    # Hidden layer with RELU activation 
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) 
    layer_2 = tf.nn.relu(layer_2) 

    # Hidden layer with RELU activation 
    layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']) 
    layer_3 = tf.nn.relu(layer_3) 

    # Hidden layer with RELU activation 
    layer_4 = tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']) 
    layer_4 = tf.nn.relu(layer_4) 

    # Output layer with linear activation 
    out_layer = tf.matmul(layer_4, weights['out']) + biases['out'] 
    return out_layer 


# Store layers weight & bias 
weights = { 
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], 0, 0.1)), 
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], 0, 0.1)), 
    'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3], 0, 0.1)), 
    'h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4], 0, 0.1)), 
    'out': tf.Variable(tf.random_normal([n_hidden_4, n_classes], 0, 0.1)) 
} 
biases = { 
    'b1': tf.Variable(tf.random_normal([n_hidden_1], 0, 0.1)), 
    'b2': tf.Variable(tf.random_normal([n_hidden_2], 0, 0.1)), 
    'b3': tf.Variable(tf.random_normal([n_hidden_3], 0, 0.1)), 
    'b4': tf.Variable(tf.random_normal([n_hidden_4], 0, 0.1)), 
    'out': tf.Variable(tf.random_normal([n_classes], 0, 0.1)) 
} 

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

# Define loss and optimizer 
cost = tf.reduce_mean(tf.square(tf.transpose(pred)-y)) 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 

# # Initializing the variables 
# init = tf.global_variables_initializer() 

# Launch the graph 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 

    # Training cycle 
    for epoch in range(training_epochs): 
     avg_cost = 0. 
     total_batch = int(total_len/batch_size) 
     # Loop over all batches 
     for i in range(total_batch-1): 
      batch_x = X_train[i*batch_size:(i+1)*batch_size] 
      batch_y = Y_train[i*batch_size:(i+1)*batch_size] 
      # Run optimization op (backprop) and cost op (to get loss value) 
      _, c, p = sess.run([optimizer, cost, pred], feed_dict={x: batch_x, 
                  y: batch_y}) 
      # Compute average loss 
      avg_cost += c/total_batch 

     # sample prediction 
     label_value = batch_y 
     estimate = p 
     err = label_value-estimate 
     print ("num batch:", total_batch) 

     # Display logs per epoch step 
     if epoch % display_step == 0: 
      print ("Epoch:", '%04d' % (epoch+1), "cost=", \ 
       "{:.9f}".format(avg_cost)) 
      print ("[*]----------------------------") 
      for i in range(3): 
       print ("label value:", label_value[i], \ 
        "estimated value:", estimate[i]) 
      print ("[*]============================") 

    print ("Optimization Finished!") 

    # Test model 
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
    # Calculate accuracy 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
    print ("Accuracy:", accuracy.eval({x: X_test, y: Y_test})) 

Answer 1

セッション外の精度を計算します。 with tf.Session() as sess:の下に移動します。