我希望每N个纪元/迭代记录一次预测，并为每个班级生成一个直方图。我的问题是如何将预测记录到一个数组（包括标签）中以生成直方图？ 如何确保每N个纪元\迭代都发生这种情况？
我已经编辑了帖子以添加代码，因此您可以看到我在说什么。最后2个代码块应以某种方式用于我所请求的内容。
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)