Python 生成TensorFlow预测的直方图

我希望每N个时代\迭代记录预测，并为每个类生成直方图。我的问题是如何将预测记录到一个数组中，包括标签，以便生成直方图？ 如何确保它在每N个时代\迭代中发生

我已经编辑了这篇文章来添加代码，这样你就可以看到我在说什么了。最后2个代码块应该以某种方式用于我请求的内容

提前谢谢

import tensorflow as tf
import numpy as np
import math
from random import random
from array import array
from ROOT import TFile, TTree, TH1D, TH2D, TBranch, vector

NUM_EXAMPLES = 1.6e4
TRAIN_SPLIT = .8
MINI_BATCH_SIZE = 1000
#NUM_EPOCHS = 3500
F_PATH = "/home/cauchy/Documents/Machine_Learning"
F_TEST = []

F_TEST += ["d3pd-ckt12rmd2030pp-G_ww_qqqq_%d%d00.root" % (1,2)]
F_TEST += ["d3pd-ckt12rmd2030pp-pyj%d.root" % (4)]
F_TEST += ["d3pd-ckt12rmd2030pp-pyj%d.root" % (5)]
F_TEST += ["d3pd-ckt12rmd2030pp-pyj%d.root" % (6)]
F_TEST += ["d3pd-ckt12rmd2030pp-pyj%d.root" % (7)]


#CALIBRATION_TARGET = "pt" # you can use pt,m,eta
INPUTS = ['m', 'grootau21', 'ysfilt', 'ungrngtrk'] # Removed pt
PT_MIN = 450 #for file 1200
PT_MAX = 730 #for file 1200
F_OUTPUT = "G1200_signaltobackground_from_pt_mass_ysfilt_grootau21_ungrngtrk.root"

N_INPUTS = len(INPUTS)

#============== inputs / target  ====================================
jet_features = []
target = []

#=================== branches for training and validation ===========
pt = []
m = []
grootau21 =[]
ysfilt = []
ungrngtrk = []

#weight = []
#================ Prepare the dataset ========================
# I need to change the data to include the multiplication by the weight (constant)
for fi in F_TEST: #Should it include background AND signal files? Yes.

  current_e = 0
  f = TFile(F_PATH + '/' + fi, 'read')
  t = TTree()
  f.GetObject("dibjet", t) # Changed from "Tree" to "dibjet"

  for entry in t:
    current_e += 1
    if current_e > NUM_EXAMPLES: # NUM_EXAMPLES should change for the different files
      break
    if (t.jet1_pt > PT_MAX or t.jet1_pt < PT_MIN): 
      continue

    tmp = []
    if 'pt'  in INPUTS: tmp += [t.jet1_pt / MAX_PT] #for file 1200
    if 'm'   in INPUTS: tmp += [t.jet1_m  / 500] #for file 1200
    if 'grootau21' in INPUTS: tmp += [t.jet1_grootau21]
    if 'ysfilt'  in INPUTS: tmp += [t.jet1_ysfilt]
    if 'ungrngtrk' in INPUTS: tmp += [t.jet1_ungrngtrk / 110] #for file 1200

    # We need only look at the class {background, signal} of the entry in terms of target

    jet_features += [tmp]

    # One-hot encoder
    if fi == 'd3pd-ckt12rmd2030pp-G_ww_qqqq_1200.root': target += [[1, 0]]
    else: target += [[0, 1]]

    pt += [t.jet1_pt]
    m += [t.jet1_m]
    grootau21 += [t.jet1_grootau21]
    ysfilt += [t.jet1_ysfilt]
    ungrngtrk += [t.jet1_ungrngtrk]


    #weight += [t.weight] 
######################################
###### prepare inputs for NN #########

trainset = list(zip(jet_features, target)) # remove ref_target?
np.random.shuffle(trainset)

jet_features, target = zip(*trainset) # What does this line do? Rearranges jetmoments\target...

total_sample = len(target)
train_size = int(total_sample*TRAIN_SPLIT)

all_x = np.float32((jet_features)) # Converts the list type? Why double paranthesis?
all_y = np.float32(target)

train_x = all_x[:train_size] # Create training\testing partitions?
test_x = all_x[train_size:]

train_y = all_y[:train_size]
test_y = all_y[train_size:]


# Define  important parameters and variable to work with the tensors
learning_rate = 0.3
training_epochs = 500
cost_history = np.empty(shape=[1], dtype=float)
n_dim = N_INPUTS
#print("n_dim", n_dim)
n_class = 2
model_path = "/home/cauchy/Documents/TensorFlow/Cuts_W" # Forgot what this path is used for

# Define the number of hidden layers and number of neurons for each layer
n_hidden_1 = 10
n_hidden_2 = 10
n_hidden_3 = 10
n_hidden_4 = 10

x = tf.placeholder(tf.float32, [None, n_dim])
W = tf.Variable(tf.zeros([n_dim, n_class]))
b = tf.Variable(tf.zeros([n_class]))
y_ = tf.placeholder(tf.float32, [None, n_class]) # Should we use a vector instead with 1 for signal and 0 for background?

# Define the model
def multilayer_perceptron(x, weights, biases):

    # Hidden layer with sigmoid activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.sigmoid(layer_1)

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

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

# Define the weights and the biases for each layer

weights = {
    'h1': tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])),
    'h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),
    'h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3])),
    'h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4])),
    'out': tf.Variable(tf.truncated_normal([n_hidden_4, n_class]))
    }

biases = {
    'b1': tf.Variable(tf.truncated_normal([n_hidden_1])),
    'b2': tf.Variable(tf.truncated_normal([n_hidden_2])),
    'b3': tf.Variable(tf.truncated_normal([n_hidden_3])),
    'b4': tf.Variable(tf.truncated_normal([n_hidden_4])),
    'out': tf.Variable(tf.truncated_normal([n_class]))
    }

# Initialize all the variables

init = tf.global_variables_initializer()

saver = tf.train.Saver()

# Call your model defined
y = multilayer_perceptron(x, weights, biases)

# Define the cost function and optimizer
cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

sess = tf.Session
sess.run(init)

# Calculate the cost and the accuracy for each epoch

mse_history = [] # mean squared error
accuracy_history = []

for epoch in range(training_epochs):
    sess.run(training_step, feed_dict={x: train_x, y_: train_y})
    cost = sess.run(cost_function, feed_dict={x: train_x, y_: train_y})
    cost_history = np.append(cost_history, cost)
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    # print("Accuracy: ", (sess.run(accuracy, feed_dict={x:test_x, y_:test_y})))
    pred_y = sess.run(y, feed_dict={x: test_x})
    mse = tf.reduce_mean(tf.square(pred_y - test_y))
    mse_ = sess.run(mse)
    mse_history.append(mse_)
    accuracy = (sess.run(accuracy, feed_dict={x: train_x, y_: train_y}))
    accuracy_history.append(accuracy)

    print('epoch: ', epoch, ' - ','cost: ', cost, " - MSE: ", mse_, "- Train Accuracy: ", accuracy)


save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Test Accuracy: ", (sess.run(accuracy, feed_dict={x: test_x, y_: test_y})))

# Print the final mean square error

pred_y = sess.run(y, feed_dict={x: test_x})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
print("MSE: $.4f" % sess.run(mse))





predictions = {
      # Generate predictions (for PREDICT and EVAL mode)
      "classes": tf.argmax(input=logits, axis=1),
      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
      # `logging_hook`.
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }
  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)


# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
    tensors=tensors_to_log, every_n_iter=50)

将tensorflow导入为tf
将numpy作为np导入
输入数学
从随机导入随机
从数组导入数组
从根导入TFile、TTree、TH1D、TH2D、TBranch、vector
NUM_示例=1.6e4
列车分离=0.8
最小批量大小=1000
#NUM_EPOCHS=3500
F_PATH=“/home/cauchy/Documents/Machine_Learning”
F_检验=[]
F_TEST+=[“d3pd-ckt12rmd2030pp-G_ww_qqqq_uqd%d00.根”%（1,2）]
F_测试+=[“d3pd-ckt12rmd2030pp-pyj%d.root”%（4）]
F_测试+=[“d3pd-ckt12rmd2030pp-pyj%d.root”%（5）]
F_测试+=[“d3pd-ckt12rmd2030pp-pyj%d.root”%（6）]
F_测试+=[“d3pd-ckt12rmd2030pp-pyj%d.root”%（7）]
#校准_TARGET=“pt”#您可以使用pt、m、eta
输入=['m'、'grootau21'、'ysfilt'、'ungrngtrk']#已删除pt
PT_MIN=450#用于文件1200
PT_MAX=730#用于文件1200
F_OUTPUT=“G1200_signaltobackground_from_pt_mass_ysfilt_grootau21_ungrngtrk.root”
N_输入=len（输入）
#================输入/目标====================================
jet_功能=[]
目标=[]
#==============================培训和验证分支机构===========
pt=[]
m=[]
grootau21=[]
ysfilt=[]
ungrngtrk=[]
#重量=[]
#======================准备数据集========================
#我需要更改数据以包含权重的乘法（常数）
对于F#U测试中的fi：#是否应包括背景和信号文件？对
当前_e=0
f=t文件（f_路径+'/'+fi，“读取”）
t=TTree（）
f、 GetObject（“dibjet”，t）#从“树”更改为“dibjet”
输入t：
电流_e+=1
如果当前_e>NUM_示例：#NUM_示例应针对不同的文件进行更改
打破
如果（t.jet1_pt>pt_MAX或t.jet1_pt