Develop Reference

ANN regression problem with high loss - Python Pandas

I try to run an artificial neural network with 2 parameters in input that can give me the value of the command.
An example of the dataset in CSV file:
P1,P2,S
7.03,3.36,787.75
6.11,3.31,491.06
5.92,3.34,480.4
5.0,3.39,469.77
5.09,3.36,481.14
5.05,3.35,502.2
4.97,3.38,200.75
5.01,3.34,464.36
5.0,3.42,475.1
4.94,3.36,448.8
4.97,3.37,750.3
5.1,3.39,344.93
5.03,3.41,199.75
5.03,3.39,484.35
5.0,3.47,483.17
4.91,3.42,485.29
3.65,3.51,513.81
5.08,3.47,443.94
5.06,3.4,473.77
5.0,3.42,535.78
3.45,3.44,483.23
4.94,3.45,449.49
4.94,3.51,345.14
5.05,3.48,2829.14
5.01,3.45,1465.58
4.96,3.45,1404.53
3.35,3.58,453.09
5.09,3.47,488.02
5.12,3.52,451.12
5.15,3.54,457.48
5.07,3.53,458.07
5.11,3.5,458.69
5.11,3.47,448.13
5.01,3.42,474.44
4.92,3.44,443.44
5.08,3.53,476.89
5.01,3.49,505.67
5.01,3.47,451.82
4.95,3.49,460.96
5.14,3.42,422.13
5.14,3.42,431.44
5.03,3.46,476.09
4.95,3.53,486.88
5.03,3.42,489.81
5.07,3.45,544.39
5.01,3.52,630.21
5.16,3.49,484.47
5.03,3.52,450.83
5.12,3.48,505.6
5.13,3.54,8400.34
4.99,3.49,615.57
5.13,3.46,673.72,
5.19,3.52,522.31
5.11,3.52,417.29
5.15,3.49,454.97
4.96,3.55,3224.72
5.12,3.54,418.85
5.06,3.53,489.87
5.05,3.45,433.04,
5.0,3.46,491.56
12.93,3.48,3280.98
5.66,3.5,428.5
4.98,3.59,586.43
4.96,3.51,427.67
5.06,3.54,508.53
4.88,3.49,1040.43
5.11,3.52,467.79
5.18,3.54,512.79
5.11,3.52,560.05
5.08,3.53,913.69
5.12,3.53,521.1
5.15,3.52,419.24
5.12,3.56,527.72
5.03,3.52,478.1
5.1,3.55,450.32
5.08,3.53,451.12
4.89,3.53,514.78
4.92,3.46,469.23
5.03,3.53,507.8
4.96,3.56,2580.22
4.99,3.52,516.24
5.0,3.55,525.96
3.66,3.61,450.69
4.91,3.53,487.98
4.97,3.54,443.86
3.53,3.57,628.8
5.02,3.51,466.91
6.41,3.46,430.19
5.0,3.58,589.98
5.06,3.55,711.22
5.26,3.55,2167.16
6.59,3.53,380.59
6.12,3.47,723.56
6.08,3.47,404.59
6.09,3.49,509.5
5.75,3.52,560.21
5.11,3.58,414.83
5.56,3.17,411.22
6.66,3.26,219.38
5.52,3.2,422.13
7.91,3.22,464.87
7.14,3.2,594.18
6.9,3.21,491.0
6.98,3.28,642.09
6.39,3.22,394.49
5.82,3.19,616.82
5.71,3.13,479.6
5.31,3.1,430.6
6.19,3.34,435.42
4.88,3.42,518.14
4.88,3.36,370.93
4.88,3.4,193.36
5.11,3.47,430.06
4.77,3.46,379.38
5.34,3.39,465.39
6.27,3.29,413.8
6.22,3.19,633.28
5.22,3.45,444.14
4.08,3.42,499.91
3.57,3.48,534.41
4.1,3.48,373.8
4.13,3.49,443.57
4.07,3.48,463.74
4.13,3.46,419.92
4.21,3.44,457.76
4.13,3.41,339.31
4.23,3.51,893.39
4.11,3.45,392.54
4.99,3.44,472.96
4.96,3.45,192.54
5.0,3.48,191.22
5.25,3.43,425.64
5.11,3.41,191.12
5.06,3.44,422.32
5.08,3.44,973.29
5.23,3.43,400.67
5.15,3.44,404.2
6.23,3.46,383.07
6.07,3.37,484.3
6.17,3.44,549.94
4.7,3.45,373.43
5.56,3.41,379.33
5.12,3.45,357.51
5.87,3.42,349.89
5.49,3.44,374.4
5.14,3.44,361.11
6.09,3.46,521.23
5.68,3.5,392.98
5.04,3.44,406.9
5.07,3.42,360.8
5.14,3.38,406.48
4.14,3.56,362.45
4.09,3.48,421.83
4.1,3.48,473.64
4.04,3.53,378.35
4.16,3.47,424.59
4.07,3.47,366.27
3.53,3.59,484.37
4.07,3.51,417.12
4.21,3.49,2521.87
4.15,3.5,458.69
4.08,3.52,402.48
4.2,3.47,373.26
3.69,3.5,486.62
4.24,3.51,402.12
4.19,3.5,414.79
4.13,3.55,390.08
4.2,3.5,452.96
4.06,3.52,524.97
4.22,3.47,442.46
4.07,3.5,403.13
4.07,3.51,404.54
4.17,3.46,393.33
4.1,3.4,430.81
4.05,3.41,365.2
4.11,3.47,412.8
4.13,3.49,431.14
4.03,3.51,417.5
3.9,3.48,386.62
4.16,3.49,351.71
5.18,3.48,351.43
4.49,3.5,336.33
3.7,3.51,551.8
6.39,3.44,369.79
6.74,3.35,408.57
6.0,3.38,2924.54
6.61,3.36,449.27
4.91,3.42,361.8
5.81,3.43,470.62
5.8,3.48,389.52
4.81,3.45,403.57
5.75,3.43,570.8
5.68,3.42,405.9
5.9,3.4,458.53
6.51,3.45,374.3
6.63,3.38,406.68
6.85,3.35,382.9
6.8,3.46,398.47
4.81,3.47,398.39
8.3,3.48,538.2
The code :
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
concatenation = pd.read_csv('concatenation.csv')
X = concatenation.iloc[:, :2].values # 2 columns
y = concatenation.iloc[:, 2].values # 1 column
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
model = Sequential()
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs= 1000)
But I have a problem during the training, I have high loss, I can not understand why?
Epoch 1/1000
10/10 [==============================] - 1s 22ms/step - loss: 407736.7188 - mae: 431.3878 - val_loss: 269746.6875 - val_mae: 380.4598
Epoch 2/1000
10/10 [==============================] - 0s 7ms/step - loss: 407391.1875 - mae: 431.0146 - val_loss: 269452.0625 - val_mae: 380.0934
Epoch 3/1000
10/10 [==============================] - 0s 8ms/step - loss: 407016.3750 - mae: 430.5912 - val_loss: 269062.3125 - val_mae: 379.6077
Epoch 4/1000
10/10 [==============================] - 0s 7ms/step - loss: 406472.7188 - mae: 430.0183 - val_loss: 268508.0312 - val_mae: 378.9190
Epoch 5/1000
10/10 [==============================] - 0s 9ms/step - loss: 405686.1562 - mae: 429.1566 - val_loss: 267709.7812 - val_mae: 377.9213
...
I checked that I didn't have a null value, I standardized my X_train
I didn't touch the outputs and I am well in case of regression with the right optimizer and the right loss function... so I can't understand why

Regression model output looks inconsistent

Some context about my project: I intend to study various parameters about bullets and how they affect the ballistics coefficient (i.e. bullet performance) of the projectile. I have different parameters, such as weight, caliber, sectional density, etc. I feel that I did this all wrong though; I am just reading through tutorials and applying what I feel could be useful and relevant in my project.
The output of my regression model looks a bit off to me; the trained model continuously outputs 0.0201 as MSE throughout the model.fit() part of my program.
Also, the model.predict(X) seems to have an accuracy of 100%, however, this does not seem right; I borrowed some code from a tutorial describing Keras models to display the model output while displaying the expected output.
This is the program constructing the model and training it
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.utils import shuffle
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard
from pandas.plotting import scatter_matrix
import time
name = 'Bullet Database Analysis v2-{}'.format(int(time.time()))
tensorboard = TensorBoard(log_dir='logs/{}'.format(name))
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
df = pd.read_csv('Bullet Optimization\ShootForum Bullet DB_2.csv')
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
dataset = df.values
X = dataset[:,0:12]
X = np.asarray(X).astype(np.float32)
y = dataset[:,13]
y = np.asarray(y).astype(np.float32)
X_train, X_val_and_test, y_train, y_val_and_test = train_test_split(X, y, test_size=0.3, shuffle=True)
X_val, X_test, y_val, y_test = train_test_split(X_val_and_test, y_val_and_test, test_size=0.5)
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization
model = Sequential(
[
#2430 is the shape of X_train
#BatchNormalization(axis=-1, momentum = 0.1),
Dense(2430, activation='relu'),
Dense(32, activation='relu'),
Dense(1),
]
)
model.compile(loss='mse', metrics=['mse'])
history = model.fit(X_train, y_train,
batch_size=64,
epochs=20,
validation_data=(X_val, y_val),
#callbacks = [tensorboard]
)
# plt.plot(history.history['loss'],'r')
# plt.plot(history.history['val_loss'],'m')
plt.plot(history.history['mse'],'b')
plt.show()
model.summary()
model.save("Bullet Optimization\Bullet Database Analysis.h5")
Here is my code, loading my previously trained model via h5
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras.models import load_model
import pandas as pd
df = pd.read_csv('Bullet Optimization\ShootForum Bullet DB_2.csv')
model = load_model('Bullet Optimization\Bullet Database Analysis.h5')
dataset = df.values
X = dataset[:,0:12]
y = dataset[:,13]
model.fit(X,y, epochs=10)
#predictions = np.argmax(model.predict(X), axis=-1)
predictions = model.predict(X)
# summarize the first 5 cases
for i in range(5):
print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i]))
This is the output
Epoch 1/10
2021-03-09 10:38:06.372303: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-03-09 10:38:07.747241: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
109/109 [==============================] - 2s 4ms/step - loss: 0.0201 - mse: 0.0201
Epoch 2/10
109/109 [==============================] - 1s 5ms/step - loss: 0.0201 - mse: 0.0201
Epoch 3/10
109/109 [==============================] - 0s 4ms/step - loss: 0.0201 - mse: 0.0201
Epoch 4/10
109/109 [==============================] - 0s 5ms/step - loss: 0.0201 - mse: 0.0201
Epoch 5/10
109/109 [==============================] - 1s 5ms/step - loss: 0.0201 - mse: 0.0201
Epoch 6/10
109/109 [==============================] - 1s 5ms/step - loss: 0.0201 - mse: 0.0201
Epoch 7/10
109/109 [==============================] - 1s 5ms/step - loss: 0.0201 - mse: 0.0201
Epoch 8/10
109/109 [==============================] - 0s 4ms/step - loss: 0.0201 - mse: 0.0201
Epoch 9/10
109/109 [==============================] - 1s 5ms/step - loss: 0.0201 - mse: 0.0201
Epoch 10/10
109/109 [==============================] - 0s 4ms/step - loss: 0.0201 - mse: 0.0201
[0.314, 7.9756, 100.0, 100.0, 31.4, 0.00314, 318.4713376, 6.480041472000001, 0.51, 12.95400001, 4.067556004, 0.145] => 0 (expected 0)
[0.358, 9.0932, 148.0, 148.0, 52.983999999999995, 0.002418919, 413.4078212, 9.590461379, 0.635, 16.12900002, 5.774182006, 0.165] => 0 (expected 0)
[0.313, 7.9502, 83.0, 83.0, 25.979, 0.003771084, 265.1757188, 5.378434422000001, 0.504, 12.80160001, 4.006900804, 0.121] => 0 (expected 0)
[0.251, 6.3754, 50.0, 50.0, 12.55, 0.00502, 199.20318730000002, 3.2400207360000004, 0.4, 10.16000001, 2.5501600030000002, 0.113] => 0 (expected 0)
[0.251, 6.3754, 50.0, 50.0, 12.55, 0.00502, 199.20318730000002, 3.2400207360000004, 0.41, 10.41400001, 2.613914003, 0.113] => 0 (expected 0)
Here is a link to my training dataset. Within my code, I used train_test_split to create both the test and train dataset.
Lastly, is there a way within Tensorboard to visualize the model fitting with the dataset? I really feel that although my model is training, it is not making any significant fitting even though the MSE error is reduced.

Because you have nan values in your dataset. Before splitting up you can check it with df.isna().sum(). These can have a negative impact on your network. Here I just simply dropped them (df.dropna(inplace = True, axis = 0)) but you can use some imputation techniques to replace them.
Also 2430 neurons can be overkill for this data, start with less neurons.
model = tf.keras.models.Sequential(
[
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1),
]
)
Here is the last epoch:
Epoch 20/20
27/27 [==============================] - 0s 8ms/step - loss: 8.2077e-04 - mse: 8.2077e-04 -
val_loss: 8.5023e-04 - val_mse: 8.5023e-04
While doing regression, calculating accuracy straight forward is not a valid option. You can use model.evaluate(X_test, y_test) or when you get predictions by model.predict, you can use other regression metrics to compute how close your predictions are.

Loss is very big from the beginning, and almost not decreasing in tensorflow

I am trying to perform linear regression in tensorflow. I don't understand why from the beginning my loss is so big. What is more, the decrease in loss is very small during training.
Could you please tell me if there are any mistakes in my code that may cause this problem?
If it is important, I used Kaggle dataset - "House price prediction" - which can be found at kaggle at /shree1992/housedata.
I paste my code below:
import sys
import tensorflow as tf
import numpy as np
import pandas as pd
np.set_printoptions(threshold=sys.maxsize)
import numpy as np
import matplotlib.pyplot as plt
# Loading and inspecting the data:
data = pd.read_csv('dataset/data.csv')
print(data.head(1))
data = data.drop('street', 1)
data = data.drop('statezip', 1)
print(data.head(1))
# Encoding categorical data - street, city, statezip, country:
data = pd.get_dummies(data, columns=["city", "country"])
data = data.values
X = np.array(data[:, 2:], dtype=np.float)
Y = np.array(data[:, 1], dtype=np.float)
print(X.shape)
# it had 4600 observations, 4659 features before dropping two columns - (4600, 57)
#after dropping 'street' and 'statezip' - (4600, 57)
# Splitting the data into training and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)
N, D = X_train.shape
# Scaling the data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Creating a model
model = tf.keras.models.Sequential([
tf.keras.layers.Input(shape=(D,)),
tf.keras.layers.Dense(1)
])
model.compile(optimizer=tf.keras.optimizers.SGD(0.001, 0.9), loss='mse')
# model.compile(optimizer='adam', loss='mse')
# learning rate scheduler
def schedule(epoch, lr):
if epoch >= 50:
return 0.0001
return 0.001
scheduler = tf.keras.callbacks.LearningRateScheduler(schedule)
# Train the model
r = model.fit(X_train, y_train, epochs=200, callbacks=[scheduler])
Below you can find a fragment of results of running this code:
Epoch 38/200
101/101 [==============================] - 0s 464us/step - loss: 270232453120.0000
Epoch 39/200
101/101 [==============================] - 0s 464us/step - loss: 270549336064.0000
Epoch 40/200
101/101 [==============================] - 0s 464us/step - loss: 270402207744.0000
Epoch 41/200
101/101 [==============================] - 0s 619us/step - loss: 270913732608.0000
Epoch 42/200
101/101 [==============================] - 0s 464us/step - loss: 271168536576.0000
Epoch 43/200
101/101 [==============================] - 0s 464us/step - loss: 270916206592.0000
Epoch 44/200
101/101 [==============================] - 0s 464us/step - loss: 272359129088.0000
Epoch 45/200
101/101 [==============================] - 0s 619us/step - loss: 271552167936.0000
Epoch 46/200
101/101 [==============================] - 0s 464us/step - loss: 272676618240.0000
Epoch 47/200
101/101 [==============================] - 0s 619us/step - loss: 272397254656.0000
Epoch 48/200
101/101 [==============================] - 0s 464us/step - loss: 270996291584.0000
Epoch 49/200
101/101 [==============================] - 0s 619us/step - loss: 271571435520.0000
Epoch 50/200
101/101 [==============================] - 0s 464us/step - loss: 272347545600.0000
Epoch 51/200
101/101 [==============================] - 0s 619us/step - loss: 269679640576.0000

Keras Multi-layer Neural Network Accuracy

I've built a simplistic multi-layer NN using Keras with precipitation data in Australia. The code takes 4 input columns: ['MinTemp', 'MaxTemp', 'Rainfall', 'WindGustSpeed'] and trains against the RainTomorrow output.
I've partitioned the data into training/test buckets, transformed all values into 0 <= n <= 1. When I trying to run model.fit, my loss values steady at ~13.2, but my accuracy is always 0.0. An example of logged fitting intervals are:
...
Epoch 37/200
113754/113754 [==============================] - 0s 2us/step - loss: -13.1274 - acc: 0.0000e+00 - val_loss: -16.1168 - val_acc: 0.0000e+00
Epoch 38/200
113754/113754 [==============================] - 0s 2us/step - loss: -13.1457 - acc: 0.0000e+00 - val_loss: -16.1168 - val_acc: 0.0000e+00
Epoch 39/200
113754/113754 [==============================] - 0s 2us/step - loss: -13.1315 - acc: 0.0000e+00 - val_loss: -16.1168 - val_acc: 0.0000e+00
Epoch 40/200
113754/113754 [==============================] - 0s 2us/step - loss: -13.1797 - acc: 0.0000e+00 - val_loss: -16.1168 - val_acc: 0.0000e+00
Epoch 41/200
113754/113754 [==============================] - 0s 2us/step - loss: -13.1844 - acc: 0.0000e+00 - val_loss: -16.1169 - val_acc: 0.0000e+00
Epoch 42/200
113754/113754 [==============================] - 0s 2us/step - loss: -13.2205 - acc: 0.0000e+00 - val_loss: -16.1169 - val_acc: 0.0000e+00
Epoch 43/200
...
How can I amend the following script, so my accuracy grows, and my predication output returns a value between 0 and 1 (0: no rain, 1: rain)?
import keras
import sklearn.model_selection
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
labelencoder = LabelEncoder()
# read data, replace NaN with 0.0
csv_data = pd.read_csv('weatherAUS.csv', header=0)
csv_data = csv_data.replace(np.nan, 0.0, regex=True)
# Input/output columns scaled to 0<=n<=1
x = csv_data.loc[:, ['MinTemp', 'MaxTemp', 'Rainfall', 'WindGustSpeed']]
y = labelencoder.fit_transform(csv_data['RainTomorrow'])
scaler_x = MinMaxScaler(feature_range =(-1, 1))
x = scaler_x.fit_transform(x)
scaler_y = MinMaxScaler(feature_range =(-1, 1))
y = scaler_y.fit_transform([y])[0]
# Partitioned data for training/testing
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.2)
# model
model = keras.models.Sequential()
model.add( keras.layers.normalization.BatchNormalization(input_shape=tuple([x_train.shape[1]])))
model.add(keras.layers.core.Dense(4, activation='relu'))
model.add(keras.layers.core.Dropout(rate=0.5))
model.add(keras.layers.normalization.BatchNormalization())
model.add(keras.layers.core.Dense(4, activation='relu'))
model.add(keras.layers.core.Dropout(rate=0.5))
model.add(keras.layers.normalization.BatchNormalization())
model.add(keras.layers.core.Dense(4, activation='relu'))
model.add(keras.layers.core.Dropout(rate=0.5))
model.add(keras.layers.core.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=["accuracy"])
callback_early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='auto')
model.fit(x_train, y_train, batch_size=1024, epochs=200, validation_data=(x_test, y_test), verbose=1, callbacks=[callback_early_stopping])
y_test = model.predict(x_test.values)

As you can see, the sigmoid activation function that you are using in your neural network output (the last layer) range from 0 to 1.
Note that your label (y) is rescaled to -1 to 1.
I suggest you change the y range to 0 to 1 and keep the sigmoid output.

So the sigmoid Ranges from 0 to 1.
Your MinMaxscaler scales data from -1 to 1.
You can fix it by replacing 'sigmoid' in the output layer with 'tanh', as tanh has output ranging from -1 to 1

Both the other answers can be used to address the fact that your network ouput is not in the same range as your y vector values. Either adjust your final layer to a tanh activation, or change the y-vector range to [0,1].
However, your network loss function and metric is defined for classification purposes, where as you are attempting regression (continuous values between [-1, 1]). The most common loss function and accuracy metric to use is the mean sqaured error, or mean absolute errtr. So I suggest you change the following:
model.compile(loss='mse', optimizer='rmsprop', metrics=['mse, 'mae'])

Keras model.predict always predicts 1

I'm working on some Artificial Intelligence project and I want to predict the bitcoin trend but while using the model.predict function from Keras with my test_set, the prediction is always equal to 1 and the line in my diagram is therefor always straight.
import csv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from cryptory import Cryptory
from keras.models import Sequential, Model, InputLayer
from keras.layers import LSTM, Dropout, Dense
from sklearn.preprocessing import MinMaxScaler
def format_to_3d(df_to_reshape):
reshaped_df = np.array(df_to_reshape)
return np.reshape(reshaped_df, (reshaped_df.shape[0], 1, reshaped_df.shape[1]))
crypto_data = Cryptory(from_date = "2014-01-01")
bitcoin_data = crypto_data.extract_coinmarketcap("bitcoin")
sc = MinMaxScaler()
for col in bitcoin_data.columns:
if col != "open":
del bitcoin_data[col]
training_set = bitcoin_data;
training_set = sc.fit_transform(training_set)
# Split the data into train, validate and test
train_data = training_set[365:]
# Split the data into x and y
x_train, y_train = train_data[:len(train_data)-1], train_data[1:]
model = Sequential()
model.add(LSTM(units=4, input_shape=(None, 1))) # 128 -- neurons**?
# model.add(Dropout(0.2))
model.add(Dense(units=1, activation="softmax")) # activation function could be different
model.compile(optimizer="adam", loss="mean_squared_error") # mse could be used for loss, look into optimiser
model.fit(format_to_3d(x_train), y_train, batch_size=32, epochs=15)
test_set = bitcoin_data
test_set = sc.transform(test_set)
test_data = test_set[:364]
input = test_data
input = sc.inverse_transform(input)
input = np.reshape(input, (364, 1, 1))
predicted_result = model.predict(input)
print(predicted_result)
real_value = sc.inverse_transform(input)
plt.plot(real_value, color='pink', label='Real Price')
plt.plot(predicted_result, color='blue', label='Predicted Price')
plt.title('Bitcoin Prediction')
plt.xlabel('Time')
plt.ylabel('Prices')
plt.legend()
plt.show()
The training set performance looks like this:
1566/1566 [==============================] - 3s 2ms/step - loss: 0.8572
Epoch 2/15
1566/1566 [==============================] - 1s 406us/step - loss: 0.8572
Epoch 3/15
1566/1566 [==============================] - 1s 388us/step - loss: 0.8572
Epoch 4/15
1566/1566 [==============================] - 1s 388us/step - loss: 0.8572
Epoch 5/15
1566/1566 [==============================] - 1s 389us/step - loss: 0.8572
Epoch 6/15
1566/1566 [==============================] - 1s 392us/step - loss: 0.8572
Epoch 7/15
1566/1566 [==============================] - 1s 408us/step - loss: 0.8572
Epoch 8/15
1566/1566 [==============================] - 1s 459us/step - loss: 0.8572
Epoch 9/15
1566/1566 [==============================] - 1s 400us/step - loss: 0.8572
Epoch 10/15
1566/1566 [==============================] - 1s 410us/step - loss: 0.8572
Epoch 11/15
1566/1566 [==============================] - 1s 395us/step - loss: 0.8572
Epoch 12/15
1566/1566 [==============================] - 1s 386us/step - loss: 0.8572
Epoch 13/15
1566/1566 [==============================] - 1s 385us/step - loss: 0.8572
Epoch 14/15
1566/1566 [==============================] - 1s 393us/step - loss: 0.8572
Epoch 15/15
1566/1566 [==============================] - 1s 397us/step - loss: 0.8572
I'm supposed to print a plot with the Real Price and the Predicted Price, the Real Price is displayed properly but the Predicted price is only a straight line because of that model.predict that only contains the value 1.
Thanks in advance!

You're trying to predict a price value, that is, you're aiming at solving a regression problem and not a classification problem.
However, in your last layer of the network (model.add(Dense(units=1, activation="softmax"))), you have a single neuron (which would be adequate for a regression problem), but you've chosen to use a softmax activation function. The softmax function is used in multi-class classification problems, to normalize the outputs into a probability distribution. If you have a single output neuron and you apply softmax, the final result will always 1.0, as it is the only parameter of the probability distribution.
In summary, for regression problems you do not use an activation function, as the network is intended to already output the predicted value.