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Evaluation accuracy stays the same while test accuracy increases just fine

I have tried over and over with different approaches to building this model however I continue to run into this issue where my training accuracy steadily increases just fine but my validation and evaluation accuracy remains very low (55% - 65%).
Epoch 95/100
119/119 [==============================] - 0s 2ms/step - loss: 0.6326 - accuracy: 0.8057 - val_loss: 2.0461 - val_accuracy: 0.5985
Epoch 96/100
119/119 [==============================] - 0s 2ms/step - loss: 0.6485 - accuracy: 0.7990 - val_loss: 1.9512 - val_accuracy: 0.5909
Epoch 97/100
119/119 [==============================] - 0s 2ms/step - loss: 0.6263 - accuracy: 0.8032 - val_loss: 2.0344 - val_accuracy: 0.5682
Epoch 98/100
119/119 [==============================] - 0s 2ms/step - loss: 0.6249 - accuracy: 0.7990 - val_loss: 2.0183 - val_accuracy: 0.5682
Epoch 99/100
119/119 [==============================] - 0s 2ms/step - loss: 0.6189 - accuracy: 0.8007 - val_loss: 2.0818 - val_accuracy: 0.5758
Epoch 100/100
119/119 [==============================] - 0s 2ms/step - loss: 0.6261 - accuracy: 0.8024 - val_loss: 2.0591 - val_accuracy: 0.5833
18/18 [==============================] - 0s 1ms/step - loss: 2.2385 - accuracy: 0.5628
EVAL:
[2.238506317138672, 0.5628318786621094]
The entire script is as follows:
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from keras import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
import numpy as np
from keras.utils import to_categorical
def count_classes(y: list):
counts = {}
for i in y:
if i in counts.keys():
counts[i] += 1
else:
counts[i] = 1
return counts
dataset = pd.read_csv('Dementia-data.csv')
X= dataset.iloc[:,1:]
y= dataset.iloc[:,0] # roughly 2562 input variables
X.head(2)
#standardizing the input feature
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle = True, random_state = 0, test_size=0.2)
print("TRAIN:")
print(count_classes(list(y_train)))
print("TEST:")
print(count_classes(list(y_test)))
sc = StandardScaler()
scaler = sc.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
optimizer = Adam(lr=0.00005)
classifier = Sequential()
#First Hidden Layer
classifier.add(Dense(32, activation='relu', kernel_initializer='random_normal', kernel_regularizer=regularizers.l2(0.005), input_dim=2562))
#Second Hidden Layer
classifier.add(Dropout(0.2))
classifier.add(Dense(64, activation='relu', kernel_initializer='random_normal', kernel_regularizer=regularizers.l2(0.005)))
#Output Layer
classifier.add(Dropout(0.2))
classifier.add(Dense(64, activation='relu', kernel_initializer='random_normal'))
classifier.add(Dense(7, activation='softmax', kernel_initializer='random_normal'))
#Compiling the neural network
classifier.compile(optimizer =optimizer,loss='sparse_categorical_crossentropy', metrics =['accuracy'])
#Fitting the data to the training dataset
classifier.fit(X_train,y_train, batch_size=20, epochs=200, validation_split=0.1, shuffle=True)
eval_model=classifier.evaluate(X_test, y_test)
print("EVAL:")
print(eval_model)
I have tried all sorts of things to try to combat overfittings such as regulators, dropouts, and data splitting because that seems to be the lead cause for problems like this one. I do not know if I am missing something.
The class distribution for the test and train data is as follows, it doesn't look like there is any inconsistencies between the 2 datasets in terms of ratios of classses:
TRAIN:
{2: 822, 4: 136, 6: 229, 5: 184, 3: 76, 1: 57}
TEST:
{2: 199, 6: 59, 5: 45, 1: 26, 3: 15, 4: 33}
The plotted accuracy for training and testing:
The plotted loss for training and testing, the loss clearly increases for the testing data:
I have been struggling with this for weeks now and I'd truly appreciate any help to get this working. I have also tried using learning rates between 0.05 and 0.00005 with little to no improvement.

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

What is the calculation process of loss functions in multi-class multi-label classification problems using deep learning?

Dataset description:
(1) X_train: (6000,4) shape
(2) y_train: (6000,4) shape
(3) X_validation: (2000,4) shape
(4) y_validation: (2000,4) shape
(5) X_test: (2000,4) shape
(6) y_test: (2000,4) shape
Relationship between X and Y is shown here
For single label classification, the activation function of the last layer is Softmax and the loss function is categorical_crossentrop.
And I know the mathematical calculation method for the loss function.
And for multi-class multi-label classification problems, the activation function of the last layer is sigmoid, and the loss function is binary_crossentrop.
I want to know how the mathematical calculation method of the loss function works
It would be a great help to me if you let me know.
def MinMaxScaler(data):
numerator = data - np.min(data)
denominator = np.max(data) - np.min(data)
return numerator / (denominator + 1e-5)
kki = pd.read_csv(filename,names=['UE0','UE1','UE2','UE3','selected_UE0','selected_UE1','selected_UE2','selected_UE3'])
print(kki)
def LoadData(file):
xy = np.loadtxt(file, delimiter=',', dtype=np.float32)
print("Data set length:", len(xy))
tr_set_size = int(len(xy) * 0.6)
xy[:, 0:-number_of_UEs] = MinMaxScaler(xy[:, 0:-number_of_UEs]) #number_of_UES : 4
X_train = xy[:tr_set_size, 0: -number_of_UEs] #6000 row
y_train = xy[:tr_set_size, number_of_UEs:number_of_UEs*2]
X_valid = xy[tr_set_size:int((tr_set_size/3) + tr_set_size), 0:-number_of_UEs]
y_valid = xy[tr_set_size:int((tr_set_size/3) + tr_set_size), number_of_UEs:number_of_UEs *2]
X_test = xy[int((tr_set_size/3) + tr_set_size):, 0:-number_of_UEs]
y_test = xy[int((tr_set_size/3) + tr_set_size):, number_of_UEs:number_of_UEs*2]
print("Training X shape:", X_train.shape)
print("Training Y shape:", y_train.shape)
print("validation x shape:", X_valid.shape)
print("validation y shape:", y_valid.shape)
print("Test X shape:", X_test.shape)
print("Test Y shape:", y_test.shape)
return X_train, y_train, X_valid, y_valid, X_test, y_test, tr_set_size
X_train, y_train, X_valid, y_valid, X_test, y_test, tr_set_size = LoadData(filename)
model = Sequential()
model.add(Dense(64,activation='relu', input_shape=(X_train.shape[1],)))
model.add(Dense(46, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(4, activation= 'sigmoid'))
model.compile( loss ='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5, batch_size=1, verbose= 1, validation_data=(X_valid, y_valid), callbacks= es)
This is a learning process, and even if epochs are repeated,
Accuracy does not improve.
Epoch 1/10
6000/6000 [==============================] - 14s 2ms/step - loss: 0.2999 - accuracy: 0.5345 - val_loss: 0.1691 - val_accuracy: 0.5465
Epoch 2/10
6000/6000 [==============================] - 14s 2ms/step - loss: 0.1554 - accuracy: 0.4883 - val_loss: 0.1228 - val_accuracy: 0.4710
Epoch 3/10
6000/6000 [==============================] - 14s 2ms/step - loss: 0.1259 - accuracy: 0.4710 - val_loss: 0.0893 - val_accuracy: 0.4910
Epoch 4/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.1094 - accuracy: 0.4990 - val_loss: 0.0918 - val_accuracy: 0.5540
Epoch 5/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.0967 - accuracy: 0.5223 - val_loss: 0.0671 - val_accuracy: 0.5405
Epoch 6/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.0910 - accuracy: 0.5198 - val_loss: 0.0836 - val_accuracy: 0.5380
Epoch 7/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.0870 - accuracy: 0.5348 - val_loss: 0.0853 - val_accuracy: 0.5775
Epoch 8/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.0859 - accuracy: 0.5518 - val_loss: 0.0515 - val_accuracy: 0.6520
Epoch 9/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.0792 - accuracy: 0.5508 - val_loss: 0.0629 - val_accuracy: 0.4350
Epoch 10/10
6000/6000 [==============================] - 13s 2ms/step - loss: 0.0793 - accuracy: 0.5638 - val_loss: 0.0632 - val_accuracy: 0.6270

Mistake 1 -
The shape of y_train, y_validation and y_test should be (6000,), (2000,) and (2000,) respectively.
Mistake 2 -
For multi-class classification, the loss should be categorical_crossentropy and activation should be a softmax. So, change these two lines, like this:
model.add(Dense(4, activation= 'softmax'))
model.compile(loss ='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
Suggestion -
Why are you splitting data by yourself? Use scikit-learn train_test_split. This code will give you proper splits:
from sklearn.model_selection import train_test_split
x, x_test, y, y_test = train_test_split(xtrain, labels, test_size=0.2, train_size=0.8)
x_train, x_validation, y_train, y_validation = train_test_split(x, y, test_size = 0.25, train_size =0.75)

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

How can I calculate precision, recall and F1-score in Neural Network models?

I'm using Keras to predict if I'll get an output of 1 or 0. The data looks like this:
funded_amnt emp_length avg_cur_bal num_actv_rev_tl loan_status
10000 5.60088 19266 2 1
13750 5.60088 2802 6 0
26100 10.0000 19241 17 1
The target is loan_status and the features are the remaining. I've normalized the data before starting to build a Neural Network model.
Here's the shape of my training and testing data:
print(X_train.shape,Y_train.shape)
# Output: (693, 4) (693,)
print(X_test.shape,Y_test.shape)
# Output: (149, 4) (149,)
The process I followed to build the Neural Network is:
# define the keras model
model = Sequential()
model.add(Dense(4, input_dim=4,activation='relu'))
model.add(Dense(4 ,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
hist = model.fit(X_train, Y_train, validation_data=(X_test, Y_test) ,epochs=10, batch_size=2)
The output after running hist:
Epoch 1/20
693/693 [==============================] - 1s 2ms/step - loss: 0.5974 - acc: 0.7605 - val_loss: 0.5499 - val_acc: 0.7785
Epoch 2/20
693/693 [==============================] - 0s 659us/step - loss: 0.5369 - acc: 0.7778 - val_loss: 0.5380 - val_acc: 0.7785
Epoch 3/20
693/693 [==============================] - 0s 700us/step - loss: 0.5330 - acc: 0.7778 - val_loss: 0.5369 - val_acc: 0.7785
Epoch 4/20
693/693 [==============================] - 0s 670us/step - loss: 0.5316 - acc: 0.7778 - val_loss: 0.5355 - val_acc: 0.7785
Epoch 5/20
693/693 [==============================] - 0s 720us/step - loss: 0.5307 - acc: 0.7778 - val_loss: 0.5345 - val_acc: 0.7785
Epoch 6/20
693/693 [==============================] - 0s 668us/step - loss: 0.5300 - acc: 0.7778 - val_loss: 0.5339 - val_acc: 0.7785
Epoch 7/20
Now, I would like to calcuate the precision, recall and F1-score instead of just the accuracy. I've tried following this. But I keep getting the following error:
ValueError: Classification metrics can't handle a mix of binary and continuous targets
Is there another way?

Can you try the following:
import numpy as np
from keras.callbacks import Callback
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
class Metrics(Callback):
def on_train_begin(self, logs={}):
self.val_f1s = []
self.val_recalls = []
self.val_precisions = []
def on_epoch_end(self, epoch, logs={}):
val_predict = (np.asarray(self.model.predict(
self.model.validation_data[0]))).round()
val_targ = self.model.validation_data[1]
_val_f1 = f1_score(val_targ, val_predict)
_val_recall = recall_score(val_targ, val_predict)
_val_precision = precision_score(val_targ, val_predict)
self.val_f1s.append(_val_f1)
self.val_recalls.append(_val_recall)
self.val_precisions.append(_val_precision)
print(f" — val_f1: {_val_f1} — val_precision: {_val_precision} — val_recall _val_recall")
return
metrics = Metrics()
And in your code you need to use it as following:
hist = model.fit(X_train, Y_train,
validation_data=(X_test, Y_test), epochs=10,
batch_size=2, callbacks=[metrics])