TensorFlow verwirrende Ausgabe

Question

Was ich versuche zu tun ist die Vorhersage des Kreditrisikos mit dem Datensatz mit 20 Eingängen und 1 binären Ausgang. Solange es sich um ein Klassifizierungsproblem handelt (zwei Klassen: positives und negatives Kreditrisiko), entschied ich mich dafür, das TensorFlow-Klassifizierungscode-Beispiel zu nehmen und es so zu modifizieren, dass es mit dem csv-Datensatz funktioniert.

Also am Ende erwarte ich 1 oder 0 Ausgabe. Ich bekomme jedoch Zahlen, die nicht einmal knapp sind. Zum Beispiel: [[-561.7623291]]

PS: Die resultierende Spalte ist die 1st.

Hier ist mein Python TensorFlow Code:

from __future__ import print_function 

import tensorflow as tf 
import csv 
import numpy as np 
# Import MNIST data 
#from tensorflow.examples.tutorials.mnist import input_data 
#mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) 

#INPUT 
col1 = [] 
col2 = [] 
col3 = [] 
col4 = [] 
col5 = [] 
col6 = [] 
col7 = [] 
col8 = [] 
col9 = [] 
col10 = [] 
col11 = [] 
col12 = [] 
col13 = [] 
col14 = [] 
col15 = [] 
col16 = [] 
col17 = [] 
col18 = [] 
col19 = [] 
col20 = [] 
col21 = [] 
col0 = [] 

mycsv = csv.reader(open("german_credit_for_mp.csv")) 
for row in mycsv: 
    col1.append(row[0]) 
    col2.append(row[1]) 
    col3.append(row[2]) 
    col4.append(row[3]) 
    col5.append(row[4]) 
    col6.append(row[5]) 
    col7.append(row[6]) 
    col8.append(row[7]) 
    col9.append(row[8]) 
    col10.append(row[9]) 
    col11.append(row[10]) 
    col12.append(row[11]) 
    col13.append(row[12]) 
    col14.append(row[13]) 
    col15.append(row[14]) 
    col16.append(row[15]) 
    col17.append(row[16]) 
    col18.append(row[17]) 
    col19.append(row[18]) 
    col20.append(row[19]) 
    col21.append(row[20]) 
    col0.append(0) 

#INPUT 

# Parameters 
learning_rate = 0.1 
training_epochs = 100 
batch_size = 100 
display_step = 10 

# Network Parameters 
n_hidden_1 = 2 # 1st layer number of features 
n_hidden_2 = 2 # 2nd layer number of features 
n_input = 20 
n_classes = 1 

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


# 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) 
    # Output layer with linear activation 
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] 
    return out_layer 

# 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_classes])) 
} 
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_classes])) 
} 

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

# Define loss and optimizer 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=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(init) 

    # Training cycle 
    for epoch in range(training_epochs): 
     avg_cost = 0. 
     total_batch = 1000#int(mnist.train.num_examples/batch_size) 
     # Loop over all batches 
     for xr in range(1000): 
      #batch_xs, batch_ys = mnist.train.next_batch(batch_size) 
      # Run optimization op (backprop) and cost op (to get loss value) 
      feed_y = np.reshape(([col1[xr]]),(-1,1)) 
      feed_x = np.reshape(([col2[xr],col3[xr],col4[xr],col5[xr],col6[xr],col7[xr],col8[xr],col9[xr],col10[xr],col11[xr],col12[xr],col13[xr],col14[xr],col15[xr],col16[xr],col17[xr],col18[xr],col19[xr],col20[xr],col21[xr]]),(-1,20)) 
      _, c = sess.run([optimizer, cost], feed_dict={x: feed_x, y: feed_y}) 

      # Compute average loss 
      avg_cost += c/total_batch 
     # Display logs per epoch step 
     if (epoch+1) % display_step == 0: 
      print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) 
      print("Eval:", pred.eval({x: np.reshape([1,30,2,2,6350,5,5,4,3,1,4,2,31,3,2,1,3,1,1,1],(-1,20))})) 

    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, tf.float32)) 
    print("Eval:", pred.eval({x: np.reshape([1,30,2,2,6350,5,5,4,3,1,4,2,31,3,2,1,3,1,1,1],(-1,20))})) 
    #32,2,46,38,7,0,0,33,0,20,53,3,0,0 

Answer 1

Für binäre Klassifizierung Sie ein System wie die folgenden verwenden kann (ich habe diesen Lauf nicht zulassen):

... 
output_activation = tf.matmul(layer_2, weights['out']) + biases['out'] # this is what is often called the "logit" 
prediction = tf.round(tf.nn.sigmoid(output_activation)) 
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
    output_activation, y)) 
train = optimizer.minimize(loss) 

sigmoid_cross_entropy_with_logits tut einem Viele Sachen auf einmal: es ist äquivalent zur Anwendung von sigmoid auf den Eingang (was dann zu dem normalen Ergebnis eines Klassifikationsnetzwerkes führt) und dann die Kreuz-Entropie zwischen diesem und den gegebenen Zielen zu berechnen, aber dies ist effizienter Weg. Die Genauigkeit sollte dann natürlich auch unter prediction berechnet werden.