GoogleのTensorFlowを使用して余分な隠れ層を追加する

Question

TensorFlowを使用して複数ラベルの分類子を作成しようとしています。隠されたレイヤーを追加したり接続したりするのに問題があります。

私はこのチュートリアルを以下ました

：http://jrmeyer.github.io/tutorial/2016/02/01/TensorFlow-Tutorial.html

私が使用しているデータは、ワンホットにエンコードされ、UCIのアイリスデータである：

トレーニングX [105,4]

5,3.2,1.2,0.2 
5.5,3.5,1.3,0.2 
4.9,3.1,1.5,0.1 
4.4,3,1.3,0.2 
5.1,3.4,1.5,0.2 
. 
. 
. 

トレーニングY [105,3]

0,0,1 
0,0,1 
0,0,1 
0,0,1 
0,0,1 
0,0,1 
. 
. 
. 

私は[45,4]のテストデータXとYも使用しています。と[45,3]とに分けられる。

import tensorflow as tf 
import numpy as np 
import tarfile 
import os 
import matplotlib.pyplot as plt 
import time 

## Import data 
def csv_to_numpy_array(filePath, delimiter): 
    return np.genfromtxt(filePath, delimiter=delimiter, dtype=None) 

trainX = csv_to_numpy_array("Iris_training_x.csv", delimiter=",").astype(np.float32) 
trainY = csv_to_numpy_array("Iris_training_y.csv", delimiter=",").astype(np.float32) 
testX = csv_to_numpy_array("Iris_testing_x.csv", delimiter=",").astype(np.float32) 
testY = csv_to_numpy_array("Iris_testing_y.csv", delimiter=",").astype(np.float32) 


# Data Set Paramaters 
numFeatures = trainX.shape[1] 
numLabels = trainY.shape[1] 

# Training Session Parameters 
numEpochs = 1000 

learningRate = tf.train.exponential_decay(learning_rate=0.008, 
              global_step= 1, 
              decay_steps=trainX.shape[0], 
              decay_rate= 0.95, 
              staircase=True) 

# Placeholders 
X=tf.placeholder(tf.float32, [None, numFeatures]) 
y=tf.placeholder(tf.float32, [None, numLabels]) 

# Initialize our weights and biases 

Weights = tf.Variable(tf.random_normal([numFeatures, numLabels], 
             mean=0, 
             stddev=(np.sqrt(6/numFeatures + numLabels + 1)), 
             name="Weights")) 

bias = tf.Variable(tf.random_normal([1, numLabels], 
            mean=0, 
            stddev=(np.sqrt(6/numFeatures + numLabels + 1)), 
            name="bias")) 


# Prediction algorithm (feedforward) 

apply_weights_OP = tf.matmul(X, Weights, name="apply_weights") 
add_bias_OP = tf.add(apply_weights_OP, bias, name="add_bias") 
activation_OP = tf.nn.sigmoid(add_bias_OP, name="activation") 

numFeatures = activation_OP 

apply_weights_OP = tf.matmul(X, Weights, name="apply_weights") 
add_bias_OP = tf.add(apply_weights_OP, bias, name="add_bias") 
activation_OP = tf.nn.sigmoid(add_bias_OP, name="activation") 

init_OP = tf.initialize_all_variables() 


# Cost function (Mean Squeared Error) 
cost_OP = tf.nn.l2_loss(activation_OP-y, name="squared_error_cost") 

# Optimization Algorithm (Gradient Descent) 
training_OP = tf.train.GradientDescentOptimizer(learningRate).minimize(cost_OP) 

# Visualize 
epoch_values=[] 
accuracy_values=[] 
cost_values=[] 
# Turn on interactive plotting 
plt.ion() 
# Create the main, super plot 
fig = plt.figure() 
# Create two subplots on their own axes and give titles 
ax1 = plt.subplot("211") 
ax1.set_title("TRAINING ACCURACY", fontsize=18) 
ax2 = plt.subplot("212") 
ax2.set_title("TRAINING COST", fontsize=18) 
plt.tight_layout() 

# Create a tensorflow session 
sess = tf.Session() 

# Initialize all tensorflow variables 
sess.run(init_OP) 

## Ops for vizualization 
# argmax(activation_OP, 1) gives the label our model thought was most likely 
# argmax(y, 1) is the correct label 
correct_predictions_OP = tf.equal(tf.argmax(activation_OP,1),tf.argmax(y,1)) 
# False is 0 and True is 1, what was our average? 
accuracy_OP = tf.reduce_mean(tf.cast(correct_predictions_OP, "float")) 
# Summary op for regression output 
activation_summary_OP = tf.histogram_summary("output", activation_OP) 
# Summary op for accuracy 
accuracy_summary_OP = tf.scalar_summary("accuracy", accuracy_OP) 
# Summary op for cost 
cost_summary_OP = tf.scalar_summary("cost", cost_OP) 
# Summary ops to check how variables (W, b) are updating after each iteration 
weightSummary = tf.histogram_summary("Weights", Weights.eval(session=sess)) 
biasSummary = tf.histogram_summary("biases", bias.eval(session=sess)) 
# Merge all summaries 
all_summary_OPS = tf.merge_all_summaries() 
# Summary writer 
writer = tf.train.SummaryWriter("summary_logs", sess.graph_def) 

# Initialize reporting variables 
cost = 0 
diff = 1 

# Training epochs 
for i in range(numEpochs): 
    if i > 1 and diff < .0001: 
     print("change in cost %g; convergence."%diff) 
     break 
    else: 
     # Run training step 
     step = sess.run(training_OP, feed_dict={X: trainX, y: trainY}) 
     # Report occasional stats 
     if i % 10 == 0: 
      #Add epoch to epoch_values 
      epoch_values.append(i) 
      #Generate accuracy stats on test data 
      summary_results, train_accuracy, newCost = sess.run(
       [all_summary_OPS, accuracy_OP, cost_OP], 
       feed_dict={X: trainX, y: trainY} 
      ) 
      # Add accuracy to live graphing variable 
      accuracy_values.append(train_accuracy) 
      # Add cost to live graphing variable 
      cost_values.append(newCost) 
      #Write summary stats to writer 
      #writer.add_summary(summary_results, i) 
      # Re-assign values for variables 
      diff = abs(newCost - cost) 
      cost = newCost 

      #generate print statements 
      print("step %d, training accuracy %g"%(i, train_accuracy)) 
      print("step %d, cost %g"%(i, newCost)) 
      print("step %d, change in cost %g"%(i, diff)) 

      # Plot progress to our two subplots 
      accuracyLine, = ax1.plot(epoch_values, accuracy_values) 
      costLine, = ax2.plot(epoch_values, cost_values) 
      fig.canvas.draw() 
      #time.sleep(1) 


# How well do we perform on held-out test data? 
print("final accuracy on test set: %s" %str(sess.run(accuracy_OP, feed_dict={X: testX, y: testY}))) 

# Create Saver 
saver = tf.train.Saver() 
# Save variables to .ckpt file 
# saver.save(sess, "trained_variables.ckpt") 

# Close tensorflow session 
sess.close() 

問題はここにある：

は、ここに私のpythonコードである

# Prediction algorithm (feedforward) 

apply_weights_OP = tf.matmul(X, Weights, name="apply_weights") 
add_bias_OP = tf.add(apply_weights_OP, bias, name="add_bias") 
activation_OP = tf.nn.sigmoid(add_bias_OP, name="activation") 

numFeatures = activation_OP 

apply_weights_OP = tf.matmul(activation_OP, Weights, name="apply_weights") 
add_bias_OP = tf.add(apply_weights_OP, bias, name="add_bias") 
activation_OP = tf.nn.sigmoid(add_bias_OP, name="activation") 

私の理解では、1層の出力は、次の1の入力に接続しなければならないことです。私は、レイヤーの出力または入力を変更する方法を知りません。それは私にこの互換性エラーを与え続ける：

/usr/bin/python3.5 /home/marco/PycharmProjects/NN_Iris/main 
Traceback (most recent call last): 
    File "/home/marco/PycharmProjects/NN_Iris/main", line 132, in <module> 
    apply_weights_OP = tf.matmul(activation_OP, Weights, name="apply_weights") 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 1346, in matmul 
    name=name) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1271, in _mat_mul 
    transpose_b=transpose_b, name=name) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op 
    op_def=op_def) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2312, in create_op 
    set_shapes_for_outputs(ret) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1704, in set_shapes_for_outputs 
    shapes = shape_func(op) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/common_shapes.py", line 94, in matmul_shape 
    inner_a.assert_is_compatible_with(inner_b) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_shape.py", line 108, in assert_is_compatible_with 
    % (self, other)) 
ValueError: Dimensions 3 and 4 are not compatible 

Process finished with exit code 1 

2つの隠れたレイヤーを正しく接続する方法に関する提案はありますか？おかげさまで

Answer 1

あなたが1つの隠れ層と出力層と完全に接続されたネットワークを使用する場合は、その形状がどのように見えるべきかあります：

num_nodesはあなた自身を選択し、隠れ層内のノードの数である

# hidden layer 
weights_hidden = tf.Variable(tf.random_normal([numFeatures, num_nodes]) 
bias_hidden = tf.Variable(tf.random_normal([num_nodes]) 
preactivations_hidden = tf.add(tf.matmul(X, weights_hidden), bias_hidden) 
activations_hidden = tf.nn.sigmoid(preactivations_hidden) 

# output layer 
weights_output = tf.Variable(tf.random_normal([num_nodes, numLabels]) 
bias_output = tf.Variable(tf.random_normal([numLabels]) 
preactivations_output = tf.add(tf.matmul(activations_hidden, weights_output), bias_output) 

。 Xは[105, numFeatures]行列、weights_hiddenは[numFeatures, num_nodes]行列です。したがって、最初の隠れ層の出力は[105, num_nodes]です。同様に、に[num_nodes, numLabels]を乗じたものは、[105, numLabels]出力となります。