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from matplotlib import pyplot as plt | |
import numpy as np | |
def show_many(images, max_rows, max_cols, figsize=(20, 20), titles = None, titles_hspace=.1, plot_title = None): | |
fig = plt.figure(figsize=figsize, dpi=300) | |
for idx, image in enumerate(images): | |
row = idx // max_cols | |
col = idx % max_cols |
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from tensorflow.keras.preprocessing.sequence import pad_sequences | |
from tensorflow.keras.preprocessing.text import hashing_trick | |
def preprocessing(questions, questions_max_length, vocabulary_size): | |
""" | |
Stateless preprocessing the text questions to a one-hot encoding and pads to max length questions. | |
The one-hot encodings max value is the vocabulary size. | |
The padding is attached at the end of each question up to the maximal question length. | |
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${:import(org.apache.commons.lang3.builder.EqualsBuilder, | |
org.apache.commons.lang3.builder.HashCodeBuilder, | |
org.apache.commons.lang3.builder.ToStringBuilder, | |
org.apache.commons.lang3.builder.ToStringStyle)} | |
@Override | |
public boolean equals(Object aObj) { | |
if(!(aObj instanceof ${enclosing_type})) { | |
return false; | |
} |
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def test_connected_models(self): | |
input1 = Input(shape=(100,)) | |
dense1 = Dense(1)(input1) | |
model1 = Model(input1, dense1) | |
input2 = Input(shape=(200,)) | |
dense2 = Dense(2)(input2) | |
model2 = Model(input2, dense2) | |
# This will work, because there are no intermediate Inputs. |
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package de.phisad.mybudget.views.budgetplan; | |
import javax.faces.application.FacesMessage; | |
import javax.faces.context.FacesContext; | |
public class Tab { | |
private String name; | |
public Tab(String name) { |
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<?xml version="1.0" encoding="UTF-8"?> | |
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> | |
<html xmlns="http://www.w3.org/1999/xhtml"> | |
<head> | |
<meta http-equiv="refresh" content="0; URL=landing_page.xhtml" ></meta> | |
</head> | |
<body> | |
</body> | |
</html> |
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import os | |
import tensorflow as tf; | |
# Just disables the warning, doesn't enable AVX/FMA | |
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | |
def main(): | |
tf.enable_eager_execution(); | |
result = tf.reduce_sum(tf.random_normal([1000,1000])) | |
print(result) |