What is the prediction value of this LSTM neural network? - tensorflow

Question

I just implemented a LSTM,
but I'm not sure if I interpreted the structure right.
is in this context testPredict = model.predict(Xtest) the last value of the sequence, and therefore ultimately (after reversing the MinMaxscaler) the variable last_value = (testPredict[-1]) the prediction regarding the future?
from IPython.core.debugger import set_trace
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import time
import yfinance as yf
import sklearn
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import LSTM, Dense, Dropout, Flatten
from sklearn.metrics import mean_squared_error
from keras.layers import ConvLSTM2D
from keras.layers import Bidirectional
from keras.models import model_from_json
df = yf.download(tickers="BTC-USD", period="20wk", interval="60m")
df = df[["Close"]]
df["returns"] = df.Close.pct_change()
df["log_returns"] = np.log(1 + df["returns"])
df.dropna(inplace=True)
X = df[["Close", "log_returns"]].values
scaler = MinMaxScaler(feature_range=(0, 1)).fit(X)
X_scaled = scaler.transform(X)
y = [x[0] for x in X_scaled]
split = int(len(X_scaled) * 0.8)
X_train = X_scaled[:split]
X_test = X_scaled[split : len(X_scaled)]
y_train = y[:split]
y_test = y[split : len(y)]
assert len(X_train) == len(y_train)
assert len(X_test) == len(y_test)
n = 24 #analyze the last 24 prices
Xtrain = []
ytrain = []
Xtest = []
ytest = []
for i in range(n, len(X_train)):
Xtrain.append(X_train[i - n : i, : X_train.shape[1]])
ytrain.append(y_train[i])
for i in range(n, len(X_test)):
Xtest.append(X_test[i - n : i, : X_test.shape[1]])
ytest.append(y_test[i])
val = np.array(ytrain[0])
val = np.c_[val, np.zeros(val.shape)]
scaler.inverse_transform(val)
Xtrain, ytrain = (np.array(Xtrain), np.array(ytrain))
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], Xtrain.shape[2]))
Xtest, ytest = (np.array(Xtest), np.array(ytest))
Xtest = np.reshape(Xtest, (Xtest.shape[0], Xtest.shape[1], Xtest.shape[2]))
model = Sequential()
model.add(LSTM(8, return_sequences=True, input_shape=(Xtrain.shape[1], Xtrain.shape[2])))
#model.add(Bidirectional(LSTM(8, return_sequences=True, input_shape=(Xtrain.shape[1], Xtrain.shape[2]))))
model.add(LSTM(4))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss="mean_squared_error", optimizer="adam")
model.fit(Xtrain, ytrain, epochs=100, validation_data=(Xtest, ytest), batch_size=16, verbose=1)
trainPredict = model.predict(Xtrain)
testPredict = model.predict(Xtest)
trainPredict = np.c_[trainPredict, np.zeros(trainPredict.shape)]
testPredict = np.c_[testPredict, np.zeros(testPredict.shape)]
trainPredict = scaler.inverse_transform(trainPredict)
trainPredict = [x[0] for x in trainPredict]
testPredict = scaler.inverse_transform(testPredict)
testPredict = [x[0] for x in testPredict]
trainScore = mean_squared_error([x[0][0] for x in Xtrain], trainPredict, squared=False)
#print("Train Score: %.2f RMSE" % (trainScore))
testScore = mean_squared_error([x[0][0] for x in Xtest], testPredict, squared=False)
#print("Test Score: %.2f RMSE" % (testScore))
########################################################################################################################
last_value = (testPredict[-1])

Answer 1

I will show you a picture result for understanding that does not change the truth that the label and value match with the criteria but reversed order to verify the result. Sufficient data is required for the working model.
Selecting random pictures from dataset ~ 6-7 per actor in reversed order verifies that the model is working when predicting the new income of the input that is all.
Symmetric shape could tell you action to respond but it is not the future prediction, in his case to have the prediction result he selects the most matching with scores or softmax but without softmax, he also can use np.argmax working with the sequences output ( you need to see the model output )
Predicting real-time data you need to input the variables and historical as you create some report, the model learns the change of the values within scopes !
[ Sample ]: Example by prediction target you visually see that learning network can do it correctly in both-way and you can use of the networks with new input reversed order is not good examines only you don't have any data.
import os
from os.path import exists
import tensorflow as tf
import tensorflow_io as tfio
import matplotlib.pyplot as plt
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = os.path.join('F:\\datasets\\downloads\\Actors\\train\\Pikaploy', '*.tif')
PATH_2 = os.path.join('F:\\datasets\\downloads\\Actors\\train\\Candidt Kibt', '*.tif')
files = tf.data.Dataset.list_files(PATH)
files_2 = tf.data.Dataset.list_files(PATH_2)
list_file = []
list_file_actual = []
list_label = []
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
for file in files.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(1)
for file in files_2.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(9)
checkpoint_path = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\loggings.log"
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
log_dir = checkpoint_dir
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices((tf.constant(tf.cast(list_file, dtype=tf.int64), shape=(10, 1, 32, 32, 4), dtype=tf.int64),
tf.constant(list_label, shape=(10, 1, 1), dtype=tf.int64)))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
tf.keras.layers.Normalization(mean=3., variance=2.),
tf.keras.layers.Normalization(mean=4., variance=6.),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Reshape((128, 225)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(192, activation='relu'),
tf.keras.layers.Dense(10),
])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Nadam'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50 )
plt.figure(figsize=(5,2))
plt.title("Actors recognitions")
for i in range(len(list_file)):
img = tf.keras.preprocessing.image.array_to_img(
list_file[i],
data_format=None,
scale=True
)
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
predictions = model.predict(img_array)
predictions = predictions[-1:]
score = tf.nn.softmax(predictions[0])
plt.subplot(5, 2, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(list_file_actual[i])
plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" + str(list_label_actual[tf.math.argmax(score)]))
plt.show()
input('...')