6
का उपयोग करके एक अतिरिक्त छिपी हुई परत जोड़ना मैं टेन्सोरफ़्लो का उपयोग करके एक बहु-लेबल वर्गीकरण बनाने की कोशिश कर रहा हूं। हालांकि मुझे छुपी परतों को जोड़ने और जोड़ने में परेशानी हो रही है।Google के TensorFlow
मैं इस ट्यूटोरियल पीछा कर रहा था: http://jrmeyer.github.io/tutorial/2016/02/01/TensorFlow-Tutorial.html
डेटा मैं उपयोग कर रहा हूँ यूसीआई के आइरिस डेटा, एक गर्म करने के लिए एन्कोड किया गया है:
प्रशिक्षण एक्स [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
.
.
.
प्रशिक्षण वाई [105,3]
0,0,1
0,0,1
0,0,1
0,0,1
0,0,1
0,0,1
.
.
.
मैं परीक्षण डेटा एक्स और वाई का भी उपयोग कर रहा हूं जो [45,4] और क्रमशः [45,3]।
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()
समस्या यहाँ है:
यहाँ मेरी अजगर कोड है
# 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")
मेरे समझ के साथ कि एक परत के उत्पादन में अगले एक के इनपुट से कनेक्ट करना चाहिए है। मैं सिर्फ परतों के आउटपुट या इनपुट को संशोधित करने के बारे में नहीं जानता;
/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
कैसे ठीक दो छिपा परतों कनेक्ट करने के लिए पर कोई सुझाव: यह मुझे इस अनुकूलता त्रुटि दे रहता है? धन्यवाद।
समस्या से कोड यहां है: "आपके द्वारा पोस्ट किए गए पूर्ण कोड से मेल नहीं खाता है। कृपया सुनिश्चित करें कि आपने सही ढंग से पोस्ट किया है। इसके अलावा: आप अपने टीएफ-परिभाषाओं को नए लोगों के साथ क्यों लिखते हैं?" समस्या यहां है: "भाग। यह ओवरराइट में पुराने मूल्य का उपयोग करके मुझे परिभाषित नहीं करता है, परिभाषित करता है, ओवरराइड करता है। ऐसा नहीं लगता कि कोई भी आपके लिए उस कोड का पालन करेगा। – kratenko