
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler


df = pd.read_csv("IsraeliLottery.csv")


df.head()


df.tail()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4047 entries, 0 to 4046
Data columns (total 8 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   Game    4047 non-null   int64 
 1   Date    4047 non-null   object
 2   A       4047 non-null   int64 
 3   B       4047 non-null   int64 
 4   C       4047 non-null   int64 
 5   D       4047 non-null   int64 
 6   E       4047 non-null   int64 
 7   F       4047 non-null   int64 
dtypes: int64(7), object(1)
memory usage: 253.1+ KB


df.describe()


df.drop(['Game', 'Date'], axis=1, inplace=True)


df.head()


scaler = StandardScaler().fit(df.values)
transformed_dataset = scaler.transform(df.values)
transformed_df = pd.DataFrame(data=transformed_dataset, index=df.index)


transformed_df.head()


# All our games
number_of_rows = df.values.shape[0]
number_of_rows

4047


# Amount of games we need to take into consideration for prediction
window_length = 7
window_length

7


# Balls counts
number_of_features = df.values.shape[1]
number_of_features

6


X = np.empty([ number_of_rows - window_length, window_length, number_of_features], dtype=float)
X

array([[[0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        ...,
        [2.19987190e-152, 6.01099881e+175, 9.03082937e-090,
         4.82336277e+228, 5.04533095e+223, 2.35567712e+251],
        [1.27640997e-152, 5.04620669e+180, 4.95241270e+223,
         5.71606413e+180, 1.06314419e+248, 9.08367206e+223],
        [1.55541130e+161, 6.32672135e+180, 2.45652619e+198,
         1.27989698e-152, 4.08841057e+233, 5.02065932e+276]],

       [[9.10580156e+102, 1.14503156e+243, 3.68065911e+180,
         1.69375611e+190, 1.99722753e+161, 5.53083643e-311],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        ...,
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000]],

       [[0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        ...,
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
        [0.00000000e+000, 0.00000000e+000, 0.00000000e+000,
         0.00000000e+000, 0.00000000e+000, 0.00000000e+000]],

       ...,

       [[4.17649570e-316, 4.17649580e-316, 4.17649589e-316,
         4.17649599e-316, 4.17649614e-316, 4.17649629e-316],
        [4.17649644e-316, 4.17649669e-316, 4.17649723e-316,
         4.17649738e-316, 4.17649753e-316, 4.17649767e-316],
        [4.17649782e-316, 4.17649797e-316, 4.17649812e-316,
         4.17649836e-316, 4.17649891e-316, 4.17649901e-316],
        ...,
        [4.17650054e-316, 4.17650064e-316, 4.17650079e-316,
         4.17650093e-316, 4.17650108e-316, 4.17650123e-316],
        [4.17650138e-316, 4.17650163e-316, 4.17650217e-316,
         4.17650232e-316, 4.17650247e-316, 4.17650261e-316],
        [4.17650276e-316, 4.17650291e-316, 4.17650306e-316,
         4.17650331e-316, 4.17650385e-316, 4.17650395e-316]],

       [[4.17650410e-316, 4.17650424e-316, 4.17650439e-316,
         4.17650454e-316, 4.17650469e-316, 4.17650494e-316],
        [4.17650548e-316, 4.17650563e-316, 4.17650578e-316,
         4.17650592e-316, 4.17650607e-316, 4.17650622e-316],
        [4.17650637e-316, 4.17650662e-316, 4.17650716e-316,
         4.17650731e-316, 4.17650746e-316, 4.17650760e-316],
        ...,
        [4.17650914e-316, 4.17650928e-316, 4.17650943e-316,
         4.17650958e-316, 4.17650973e-316, 4.17650998e-316],
        [4.17651052e-316, 4.17651062e-316, 4.17651072e-316,
         4.17651086e-316, 4.17651101e-316, 4.17651116e-316],
        [4.17651131e-316, 4.17651156e-316, 4.17651210e-316,
         4.17651220e-316, 4.17651230e-316, 4.17651245e-316]],

       [[4.17651259e-316, 4.17651274e-316, 4.17651289e-316,
         4.17651314e-316, 4.17651368e-316, 4.17651378e-316],
        [4.17651393e-316, 4.17651408e-316, 4.17651422e-316,
         4.17651437e-316, 4.17651452e-316, 4.17651477e-316],
        [4.17651531e-316, 4.17651541e-316, 4.17651556e-316,
         4.17651571e-316, 4.17651585e-316, 4.17651600e-316],
        ...,
        [4.17651749e-316, 4.17651763e-316, 4.17651778e-316,
         4.17651803e-316, 4.17651857e-316, 4.17651872e-316],
        [4.17651887e-316, 4.17651902e-316, 4.17651917e-316,
         4.17651931e-316, 4.17651946e-316, 4.17651971e-316],
        [4.17652025e-316, 4.17652040e-316, 4.17652055e-316,
         4.17652070e-316, 4.17652084e-316, 4.17652099e-316]]])


y = np.empty([ number_of_rows - window_length, number_of_features], dtype=float)
y

array([[  0.        ,  53.31128703,   5.29670833,  22.07629815,
         22.07629815,   1.69379259],
       [ 22.07629815,  13.67921389,   7.28212963,  13.67921389,
          0.48796111,  13.67921389],
       [  7.28212963,   0.48796111,  10.89962407,  28.10545555,
         39.70837129,   7.28212963],
       ...,
       [  2.67415833,   2.67415833,   2.67415833,  13.21529991,
          5.59187516,   5.59187516],
       [135.37986626,   0.40358754,  13.21529991,   2.67415833,
          0.40358754,   1.86244595],
       [  1.86244595,   5.59187516,  31.7564415 ,   0.40358754,
          1.86244595,   0.40358754]])


for i in range(0, number_of_rows-window_length):
    X[i] = transformed_df.iloc[i : i+window_length, 0 : number_of_features]
    y[i] = transformed_df.iloc[i+window_length : i+window_length+1, 0 : number_of_features]


X.shape

(4040, 7, 6)


y.shape

(4040, 6)


X[0]

array([[-0.58223113,  0.429455  ,  0.1300936 , -0.13906023, -0.57371015,
        -0.27670816],
       [ 1.57534546,  1.43157169,  0.87667445,  0.88736948,  0.4844041 ,
        -0.10749723],
       [ 0.49655716,  0.0954161 ,  1.32462296,  0.59410385,  0.78672246,
         0.56934648],
       [-1.01374645,  0.429455  , -0.01922257,  0.44747103,  0.93788164,
         0.7385574 ],
       [-1.01374645, -0.73968114, -1.36306809, -2.04528682, -2.68993865,
        -0.78434094],
       [ 0.2807995 , -0.40564224, -1.06443575, -0.8722243 , -0.87602851,
        -0.10749723],
       [-1.01374645, -0.90670059,  0.87667445,  0.88736948,  0.63556328,
        -0.10749723]])


X[1]

array([[ 1.57534546,  1.43157169,  0.87667445,  0.88736948,  0.4844041 ,
        -0.10749723],
       [ 0.49655716,  0.0954161 ,  1.32462296,  0.59410385,  0.78672246,
         0.56934648],
       [-1.01374645,  0.429455  , -0.01922257,  0.44747103,  0.93788164,
         0.7385574 ],
       [-1.01374645, -0.73968114, -1.36306809, -2.04528682, -2.68993865,
        -0.78434094],
       [ 0.2807995 , -0.40564224, -1.06443575, -0.8722243 , -0.87602851,
        -0.10749723],
       [-1.01374645, -0.90670059,  0.87667445,  0.88736948,  0.63556328,
        -0.10749723],
       [-0.79798879,  0.26243555,  0.42872594,  0.1542054 , -0.57371015,
        -1.29197372]])


y[0]

array([-0.79798879,  0.26243555,  0.42872594,  0.1542054 , -0.57371015,
       -1.29197372])


y[1]

array([-0.58223113, -0.40564224,  1.32462296,  1.32726792,  0.78672246,
        0.23092462])


# Recurrent Neural Netowrk (RNN) with Long Short Term Memory (LSTM)
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import LSTM, Dense, Bidirectional, Dropout
batch_size = 100


# Initialising the RNN
model = Sequential()
# Adding the input layer and the LSTM layer
model.add(Bidirectional(LSTM(240,
                        input_shape = (window_length, number_of_features),
                        return_sequences = True)))
# Adding a first Dropout layer
model.add(Dropout(0.2))
# Adding a second LSTM layer
model.add(Bidirectional(LSTM(240,
                        input_shape = (window_length, number_of_features),
                        return_sequences = True)))
# Adding a second Dropout layer
model.add(Dropout(0.2))
# Adding a third LSTM layer
model.add(Bidirectional(LSTM(240,
                        input_shape = (window_length, number_of_features),
                        return_sequences = True)))
# Adding a fourth LSTM layer
model.add(Bidirectional(LSTM(240,
                        input_shape = (window_length, number_of_features),
                        return_sequences = False)))
# Adding a fourth Dropout layer
model.add(Dropout(0.2))
# Adding the first output layer
model.add(Dense(59))
# Adding the last output layer
model.add(Dense(number_of_features))


from tensorflow import keras
from tensorflow.keras.optimizers import Adam
model.compile(optimizer=Adam(learning_rate=0.0001), loss ='mse', metrics=['accuracy'])


model.fit(x=X, y=y, batch_size=100, epochs=300, verbose=2)

Epoch 1/300
41/41 - 12s - loss: 0.0094 - accuracy: 0.9245 - 12s/epoch - 285ms/step
Epoch 2/300
41/41 - 1s - loss: 0.0081 - accuracy: 0.9339 - 637ms/epoch - 16ms/step
Epoch 3/300
41/41 - 1s - loss: 0.0077 - accuracy: 0.9354 - 633ms/epoch - 15ms/step
Epoch 4/300
41/41 - 1s - loss: 0.0072 - accuracy: 0.9401 - 629ms/epoch - 15ms/step
Epoch 5/300
41/41 - 1s - loss: 0.0070 - accuracy: 0.9342 - 644ms/epoch - 16ms/step
Epoch 6/300
41/41 - 1s - loss: 0.0066 - accuracy: 0.9351 - 652ms/epoch - 16ms/step
Epoch 7/300
41/41 - 1s - loss: 0.0067 - accuracy: 0.9349 - 619ms/epoch - 15ms/step
Epoch 8/300
41/41 - 1s - loss: 0.0066 - accuracy: 0.9374 - 638ms/epoch - 16ms/step
Epoch 9/300
41/41 - 1s - loss: 0.0062 - accuracy: 0.9394 - 639ms/epoch - 16ms/step
Epoch 10/300
41/41 - 1s - loss: 0.0061 - accuracy: 0.9391 - 630ms/epoch - 15ms/step
Epoch 11/300
41/41 - 1s - loss: 0.0061 - accuracy: 0.9381 - 637ms/epoch - 16ms/step
Epoch 12/300
41/41 - 1s - loss: 0.0062 - accuracy: 0.9356 - 638ms/epoch - 16ms/step
Epoch 13/300
41/41 - 1s - loss: 0.0061 - accuracy: 0.9356 - 630ms/epoch - 15ms/step
Epoch 14/300
41/41 - 1s - loss: 0.0060 - accuracy: 0.9413 - 637ms/epoch - 16ms/step
Epoch 15/300
41/41 - 1s - loss: 0.0061 - accuracy: 0.9418 - 629ms/epoch - 15ms/step
Epoch 16/300
41/41 - 1s - loss: 0.0059 - accuracy: 0.9381 - 641ms/epoch - 16ms/step
Epoch 17/300
41/41 - 1s - loss: 0.0059 - accuracy: 0.9369 - 634ms/epoch - 15ms/step
Epoch 18/300
41/41 - 1s - loss: 0.0059 - accuracy: 0.9436 - 632ms/epoch - 15ms/step
Epoch 19/300
41/41 - 1s - loss: 0.0057 - accuracy: 0.9384 - 631ms/epoch - 15ms/step
Epoch 20/300
41/41 - 1s - loss: 0.0057 - accuracy: 0.9413 - 627ms/epoch - 15ms/step
Epoch 21/300
41/41 - 1s - loss: 0.0057 - accuracy: 0.9426 - 630ms/epoch - 15ms/step
Epoch 22/300
41/41 - 1s - loss: 0.0058 - accuracy: 0.9426 - 642ms/epoch - 16ms/step
Epoch 23/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9384 - 625ms/epoch - 15ms/step
Epoch 24/300
41/41 - 1s - loss: 0.0057 - accuracy: 0.9349 - 635ms/epoch - 15ms/step
Epoch 25/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9473 - 628ms/epoch - 15ms/step
Epoch 26/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9431 - 626ms/epoch - 15ms/step
Epoch 27/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9394 - 633ms/epoch - 15ms/step
Epoch 28/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9408 - 624ms/epoch - 15ms/step
Epoch 29/300
41/41 - 1s - loss: 0.0057 - accuracy: 0.9428 - 632ms/epoch - 15ms/step
Epoch 30/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9483 - 634ms/epoch - 15ms/step
Epoch 31/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9374 - 636ms/epoch - 16ms/step
Epoch 32/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9488 - 643ms/epoch - 16ms/step
Epoch 33/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9369 - 636ms/epoch - 16ms/step
Epoch 34/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9470 - 630ms/epoch - 15ms/step
Epoch 35/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9399 - 648ms/epoch - 16ms/step
Epoch 36/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9431 - 657ms/epoch - 16ms/step
Epoch 37/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9450 - 656ms/epoch - 16ms/step
Epoch 38/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9411 - 639ms/epoch - 16ms/step
Epoch 39/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9480 - 629ms/epoch - 15ms/step
Epoch 40/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9455 - 641ms/epoch - 16ms/step
Epoch 41/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9441 - 634ms/epoch - 15ms/step
Epoch 42/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9401 - 628ms/epoch - 15ms/step
Epoch 43/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9448 - 637ms/epoch - 16ms/step
Epoch 44/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9406 - 629ms/epoch - 15ms/step
Epoch 45/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9448 - 636ms/epoch - 16ms/step
Epoch 46/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9468 - 631ms/epoch - 15ms/step
Epoch 47/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9431 - 631ms/epoch - 15ms/step
Epoch 48/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9436 - 645ms/epoch - 16ms/step
Epoch 49/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9421 - 636ms/epoch - 16ms/step
Epoch 50/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9455 - 615ms/epoch - 15ms/step
Epoch 51/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9465 - 630ms/epoch - 15ms/step
Epoch 52/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9344 - 637ms/epoch - 16ms/step
Epoch 53/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9443 - 628ms/epoch - 15ms/step
Epoch 54/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9436 - 644ms/epoch - 16ms/step
Epoch 55/300
41/41 - 1s - loss: 0.0056 - accuracy: 0.9384 - 637ms/epoch - 16ms/step
Epoch 56/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9433 - 634ms/epoch - 15ms/step
Epoch 57/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9470 - 635ms/epoch - 15ms/step
Epoch 58/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9413 - 628ms/epoch - 15ms/step
Epoch 59/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9416 - 628ms/epoch - 15ms/step
Epoch 60/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9460 - 629ms/epoch - 15ms/step
Epoch 61/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9401 - 622ms/epoch - 15ms/step
Epoch 62/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9374 - 622ms/epoch - 15ms/step
Epoch 63/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9421 - 622ms/epoch - 15ms/step
Epoch 64/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9433 - 642ms/epoch - 16ms/step
Epoch 65/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9403 - 628ms/epoch - 15ms/step
Epoch 66/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9450 - 627ms/epoch - 15ms/step
Epoch 67/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9436 - 629ms/epoch - 15ms/step
Epoch 68/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9376 - 627ms/epoch - 15ms/step
Epoch 69/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9438 - 611ms/epoch - 15ms/step
Epoch 70/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9413 - 629ms/epoch - 15ms/step
Epoch 71/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9426 - 639ms/epoch - 16ms/step
Epoch 72/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9473 - 621ms/epoch - 15ms/step
Epoch 73/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9411 - 635ms/epoch - 15ms/step
Epoch 74/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9515 - 629ms/epoch - 15ms/step
Epoch 75/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9473 - 631ms/epoch - 15ms/step
Epoch 76/300
41/41 - 1s - loss: 0.0055 - accuracy: 0.9475 - 627ms/epoch - 15ms/step
Epoch 77/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9460 - 620ms/epoch - 15ms/step
Epoch 78/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9480 - 627ms/epoch - 15ms/step
Epoch 79/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9450 - 621ms/epoch - 15ms/step
Epoch 80/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9423 - 641ms/epoch - 16ms/step
Epoch 81/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9413 - 627ms/epoch - 15ms/step
Epoch 82/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9480 - 648ms/epoch - 16ms/step
Epoch 83/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9403 - 625ms/epoch - 15ms/step
Epoch 84/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9399 - 676ms/epoch - 16ms/step
Epoch 85/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9490 - 631ms/epoch - 15ms/step
Epoch 86/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9408 - 625ms/epoch - 15ms/step
Epoch 87/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9455 - 646ms/epoch - 16ms/step
Epoch 88/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9418 - 628ms/epoch - 15ms/step
Epoch 89/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9512 - 629ms/epoch - 15ms/step
Epoch 90/300
41/41 - 1s - loss: 0.0054 - accuracy: 0.9443 - 618ms/epoch - 15ms/step
Epoch 91/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9455 - 622ms/epoch - 15ms/step
Epoch 92/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9455 - 634ms/epoch - 15ms/step
Epoch 93/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9423 - 619ms/epoch - 15ms/step
Epoch 94/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9460 - 625ms/epoch - 15ms/step
Epoch 95/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9448 - 624ms/epoch - 15ms/step
Epoch 96/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9470 - 630ms/epoch - 15ms/step
Epoch 97/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9431 - 632ms/epoch - 15ms/step
Epoch 98/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9473 - 622ms/epoch - 15ms/step
Epoch 99/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9436 - 624ms/epoch - 15ms/step
Epoch 100/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9389 - 639ms/epoch - 16ms/step
Epoch 101/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9416 - 631ms/epoch - 15ms/step
Epoch 102/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9421 - 623ms/epoch - 15ms/step
Epoch 103/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9483 - 636ms/epoch - 16ms/step
Epoch 104/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9455 - 619ms/epoch - 15ms/step
Epoch 105/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9473 - 633ms/epoch - 15ms/step
Epoch 106/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9431 - 624ms/epoch - 15ms/step
Epoch 107/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9416 - 650ms/epoch - 16ms/step
Epoch 108/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9500 - 633ms/epoch - 15ms/step
Epoch 109/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9483 - 625ms/epoch - 15ms/step
Epoch 110/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9379 - 636ms/epoch - 16ms/step
Epoch 111/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9473 - 628ms/epoch - 15ms/step
Epoch 112/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9470 - 628ms/epoch - 15ms/step
Epoch 113/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9401 - 630ms/epoch - 15ms/step
Epoch 114/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9485 - 620ms/epoch - 15ms/step
Epoch 115/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9446 - 620ms/epoch - 15ms/step
Epoch 116/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9418 - 629ms/epoch - 15ms/step
Epoch 117/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9436 - 621ms/epoch - 15ms/step
Epoch 118/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9436 - 629ms/epoch - 15ms/step
Epoch 119/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9411 - 635ms/epoch - 15ms/step
Epoch 120/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9433 - 624ms/epoch - 15ms/step
Epoch 121/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9441 - 633ms/epoch - 15ms/step
Epoch 122/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9438 - 619ms/epoch - 15ms/step
Epoch 123/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9438 - 629ms/epoch - 15ms/step
Epoch 124/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9421 - 615ms/epoch - 15ms/step
Epoch 125/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9468 - 619ms/epoch - 15ms/step
Epoch 126/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9418 - 636ms/epoch - 16ms/step
Epoch 127/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9443 - 630ms/epoch - 15ms/step
Epoch 128/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9453 - 632ms/epoch - 15ms/step
Epoch 129/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9401 - 632ms/epoch - 15ms/step
Epoch 130/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9468 - 631ms/epoch - 15ms/step
Epoch 131/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9463 - 643ms/epoch - 16ms/step
Epoch 132/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9423 - 676ms/epoch - 16ms/step
Epoch 133/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9485 - 659ms/epoch - 16ms/step
Epoch 134/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9468 - 648ms/epoch - 16ms/step
Epoch 135/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9446 - 637ms/epoch - 16ms/step
Epoch 136/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9453 - 655ms/epoch - 16ms/step
Epoch 137/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9436 - 650ms/epoch - 16ms/step
Epoch 138/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9443 - 818ms/epoch - 20ms/step
Epoch 139/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9428 - 708ms/epoch - 17ms/step
Epoch 140/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9389 - 653ms/epoch - 16ms/step
Epoch 141/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9431 - 647ms/epoch - 16ms/step
Epoch 142/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9438 - 662ms/epoch - 16ms/step
Epoch 143/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9423 - 647ms/epoch - 16ms/step
Epoch 144/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9438 - 629ms/epoch - 15ms/step
Epoch 145/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9473 - 636ms/epoch - 16ms/step
Epoch 146/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9453 - 642ms/epoch - 16ms/step
Epoch 147/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9421 - 638ms/epoch - 16ms/step
Epoch 148/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9443 - 661ms/epoch - 16ms/step
Epoch 149/300
41/41 - 1s - loss: 0.0053 - accuracy: 0.9431 - 649ms/epoch - 16ms/step
Epoch 150/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9458 - 969ms/epoch - 24ms/step
Epoch 151/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9441 - 632ms/epoch - 15ms/step
Epoch 152/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9423 - 640ms/epoch - 16ms/step
Epoch 153/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9428 - 635ms/epoch - 15ms/step
Epoch 154/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9478 - 646ms/epoch - 16ms/step
Epoch 155/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9448 - 645ms/epoch - 16ms/step
Epoch 156/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9416 - 613ms/epoch - 15ms/step
Epoch 157/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9500 - 635ms/epoch - 15ms/step
Epoch 158/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9438 - 629ms/epoch - 15ms/step
Epoch 159/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9465 - 620ms/epoch - 15ms/step
Epoch 160/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9416 - 617ms/epoch - 15ms/step
Epoch 161/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9413 - 631ms/epoch - 15ms/step
Epoch 162/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9475 - 632ms/epoch - 15ms/step
Epoch 163/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9453 - 657ms/epoch - 16ms/step
Epoch 164/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9438 - 899ms/epoch - 22ms/step
Epoch 165/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9500 - 625ms/epoch - 15ms/step
Epoch 166/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9396 - 639ms/epoch - 16ms/step
Epoch 167/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9455 - 636ms/epoch - 16ms/step
Epoch 168/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9478 - 633ms/epoch - 15ms/step
Epoch 169/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9448 - 652ms/epoch - 16ms/step
Epoch 170/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9495 - 644ms/epoch - 16ms/step
Epoch 171/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9394 - 640ms/epoch - 16ms/step
Epoch 172/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9488 - 634ms/epoch - 15ms/step
Epoch 173/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9453 - 634ms/epoch - 15ms/step
Epoch 174/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9450 - 633ms/epoch - 15ms/step
Epoch 175/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9483 - 633ms/epoch - 15ms/step
Epoch 176/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9411 - 622ms/epoch - 15ms/step
Epoch 177/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9455 - 673ms/epoch - 16ms/step
Epoch 178/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9433 - 855ms/epoch - 21ms/step
Epoch 179/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9465 - 641ms/epoch - 16ms/step
Epoch 180/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9505 - 639ms/epoch - 16ms/step
Epoch 181/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9483 - 639ms/epoch - 16ms/step
Epoch 182/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9478 - 625ms/epoch - 15ms/step
Epoch 183/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9488 - 650ms/epoch - 16ms/step
Epoch 184/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9480 - 629ms/epoch - 15ms/step
Epoch 185/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9460 - 642ms/epoch - 16ms/step
Epoch 186/300
41/41 - 1s - loss: 0.0052 - accuracy: 0.9408 - 631ms/epoch - 15ms/step
Epoch 187/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9428 - 626ms/epoch - 15ms/step
Epoch 188/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9495 - 675ms/epoch - 16ms/step
Epoch 189/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9453 - 690ms/epoch - 17ms/step
Epoch 190/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9438 - 699ms/epoch - 17ms/step
Epoch 191/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9394 - 711ms/epoch - 17ms/step
Epoch 192/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9485 - 705ms/epoch - 17ms/step
Epoch 193/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9413 - 705ms/epoch - 17ms/step
Epoch 194/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9438 - 686ms/epoch - 17ms/step
Epoch 195/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9475 - 702ms/epoch - 17ms/step
Epoch 196/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9418 - 678ms/epoch - 17ms/step
Epoch 197/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9463 - 688ms/epoch - 17ms/step
Epoch 198/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9517 - 767ms/epoch - 19ms/step
Epoch 199/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9450 - 688ms/epoch - 17ms/step
Epoch 200/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9443 - 687ms/epoch - 17ms/step
Epoch 201/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9480 - 674ms/epoch - 16ms/step
Epoch 202/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9426 - 668ms/epoch - 16ms/step
Epoch 203/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9468 - 651ms/epoch - 16ms/step
Epoch 204/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9438 - 653ms/epoch - 16ms/step
Epoch 205/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9460 - 636ms/epoch - 16ms/step
Epoch 206/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9431 - 628ms/epoch - 15ms/step
Epoch 207/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9428 - 631ms/epoch - 15ms/step
Epoch 208/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9480 - 627ms/epoch - 15ms/step
Epoch 209/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9465 - 637ms/epoch - 16ms/step
Epoch 210/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9418 - 634ms/epoch - 15ms/step
Epoch 211/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9475 - 613ms/epoch - 15ms/step
Epoch 212/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9413 - 628ms/epoch - 15ms/step
Epoch 213/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9478 - 632ms/epoch - 15ms/step
Epoch 214/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9431 - 648ms/epoch - 16ms/step
Epoch 215/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9465 - 642ms/epoch - 16ms/step
Epoch 216/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9488 - 626ms/epoch - 15ms/step
Epoch 217/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9500 - 630ms/epoch - 15ms/step
Epoch 218/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9458 - 639ms/epoch - 16ms/step
Epoch 219/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9446 - 621ms/epoch - 15ms/step
Epoch 220/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9448 - 631ms/epoch - 15ms/step
Epoch 221/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9428 - 626ms/epoch - 15ms/step
Epoch 222/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9446 - 641ms/epoch - 16ms/step
Epoch 223/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9478 - 637ms/epoch - 16ms/step
Epoch 224/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9493 - 656ms/epoch - 16ms/step
Epoch 225/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9450 - 674ms/epoch - 16ms/step
Epoch 226/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9428 - 637ms/epoch - 16ms/step
Epoch 227/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9396 - 627ms/epoch - 15ms/step
Epoch 228/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9455 - 636ms/epoch - 16ms/step
Epoch 229/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9458 - 631ms/epoch - 15ms/step
Epoch 230/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9507 - 644ms/epoch - 16ms/step
Epoch 231/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9443 - 651ms/epoch - 16ms/step
Epoch 232/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9416 - 649ms/epoch - 16ms/step
Epoch 233/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9480 - 623ms/epoch - 15ms/step
Epoch 234/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9475 - 633ms/epoch - 15ms/step
Epoch 235/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9468 - 625ms/epoch - 15ms/step
Epoch 236/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9483 - 637ms/epoch - 16ms/step
Epoch 237/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9446 - 674ms/epoch - 16ms/step
Epoch 238/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9470 - 621ms/epoch - 15ms/step
Epoch 239/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9488 - 642ms/epoch - 16ms/step
Epoch 240/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9478 - 644ms/epoch - 16ms/step
Epoch 241/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9448 - 640ms/epoch - 16ms/step
Epoch 242/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9485 - 639ms/epoch - 16ms/step
Epoch 243/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9488 - 642ms/epoch - 16ms/step
Epoch 244/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9436 - 627ms/epoch - 15ms/step
Epoch 245/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9408 - 628ms/epoch - 15ms/step
Epoch 246/300
41/41 - 1s - loss: 0.0051 - accuracy: 0.9441 - 640ms/epoch - 16ms/step
Epoch 247/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9493 - 627ms/epoch - 15ms/step
Epoch 248/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9500 - 627ms/epoch - 15ms/step
Epoch 249/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9386 - 626ms/epoch - 15ms/step
Epoch 250/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9426 - 631ms/epoch - 15ms/step
Epoch 251/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9453 - 627ms/epoch - 15ms/step
Epoch 252/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9448 - 616ms/epoch - 15ms/step
Epoch 253/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9443 - 629ms/epoch - 15ms/step
Epoch 254/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9376 - 622ms/epoch - 15ms/step
Epoch 255/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9515 - 630ms/epoch - 15ms/step
Epoch 256/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9483 - 638ms/epoch - 16ms/step
Epoch 257/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9535 - 622ms/epoch - 15ms/step
Epoch 258/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9522 - 626ms/epoch - 15ms/step
Epoch 259/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9483 - 627ms/epoch - 15ms/step
Epoch 260/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9441 - 618ms/epoch - 15ms/step
Epoch 261/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9490 - 642ms/epoch - 16ms/step
Epoch 262/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9490 - 639ms/epoch - 16ms/step
Epoch 263/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9468 - 625ms/epoch - 15ms/step
Epoch 264/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9478 - 628ms/epoch - 15ms/step
Epoch 265/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9446 - 625ms/epoch - 15ms/step
Epoch 266/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9465 - 622ms/epoch - 15ms/step
Epoch 267/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9438 - 619ms/epoch - 15ms/step
Epoch 268/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9470 - 629ms/epoch - 15ms/step
Epoch 269/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9517 - 637ms/epoch - 16ms/step
Epoch 270/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9438 - 619ms/epoch - 15ms/step
Epoch 271/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9483 - 631ms/epoch - 15ms/step
Epoch 272/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9421 - 649ms/epoch - 16ms/step
Epoch 273/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9436 - 662ms/epoch - 16ms/step
Epoch 274/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9436 - 633ms/epoch - 15ms/step
Epoch 275/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9465 - 635ms/epoch - 15ms/step
Epoch 276/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9443 - 620ms/epoch - 15ms/step
Epoch 277/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9498 - 672ms/epoch - 16ms/step
Epoch 278/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9446 - 649ms/epoch - 16ms/step
Epoch 279/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9450 - 630ms/epoch - 15ms/step
Epoch 280/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9431 - 629ms/epoch - 15ms/step
Epoch 281/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9443 - 632ms/epoch - 15ms/step
Epoch 282/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9530 - 634ms/epoch - 15ms/step
Epoch 283/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9416 - 633ms/epoch - 15ms/step
Epoch 284/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9465 - 620ms/epoch - 15ms/step
Epoch 285/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9458 - 627ms/epoch - 15ms/step
Epoch 286/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9460 - 623ms/epoch - 15ms/step
Epoch 287/300
41/41 - 1s - loss: 0.0050 - accuracy: 0.9473 - 636ms/epoch - 16ms/step
Epoch 288/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9433 - 640ms/epoch - 16ms/step
Epoch 289/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9455 - 627ms/epoch - 15ms/step
Epoch 290/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9453 - 631ms/epoch - 15ms/step
Epoch 291/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9463 - 650ms/epoch - 16ms/step
Epoch 292/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9483 - 619ms/epoch - 15ms/step
Epoch 293/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9490 - 624ms/epoch - 15ms/step
Epoch 294/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9453 - 644ms/epoch - 16ms/step
Epoch 295/300
41/41 - 1s - loss: 0.0048 - accuracy: 0.9460 - 628ms/epoch - 15ms/step
Epoch 296/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9455 - 636ms/epoch - 16ms/step
Epoch 297/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9510 - 630ms/epoch - 15ms/step
Epoch 298/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9460 - 630ms/epoch - 15ms/step
Epoch 299/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9441 - 634ms/epoch - 15ms/step
Epoch 300/300
41/41 - 1s - loss: 0.0049 - accuracy: 0.9525 - 622ms/epoch - 15ms/step

<keras.callbacks.History at 0x7faa82146350>


to_predict = df.tail(8)
to_predict.drop([to_predict.index[-1]],axis=0, inplace=True)
to_predict

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,


to_predict = np.array(to_predict)
to_predict

array([[ 1, 14, 15, 17, 28, 37],
       [ 3, 13, 21, 24, 27, 35],
       [17, 19, 21, 23, 25, 34],
       [ 9, 16, 17, 21, 26, 29],
       [ 1,  4,  5, 24, 31, 32],
       [ 1,  8, 18, 25, 29, 30],
       [ 3,  4,  5, 15, 24, 33]])


scaled_to_predict = scaler.transform(to_predict)


y_pred = model.predict(np.array([scaled_to_predict]))
print("The predicted numbers in the last lottery game are:", scaler.inverse_transform(y_pred).astype(int)[0])

The predicted numbers in the last lottery game are: [ 5  9 12 20 23 35]


prediction = df.tail(1)
prediction = np.array(prediction)
print("The actual numbers in the last lottery game were:", prediction[0])

The actual numbers in the last lottery game were: [ 6 10 13 20 23 35]

	Game	A	B	C	D	E	F
count	4047.000000	4047.000000	4047.000000	4047.000000	4047.000000	4047.000000	4047.000000
mean	4695.399061	5.698542	11.428713	17.128737	22.948357	28.795404	34.635285
std	3106.179305	4.635402	5.988067	6.698026	6.820599	6.616360	5.910514
min	1035.000000	1.000000	2.000000	3.000000	4.000000	7.000000	13.000000
25%	2046.500000	2.000000	7.000000	12.000000	18.000000	24.000000	31.000000
50%	3058.000000	4.000000	10.000000	17.000000	23.000000	29.000000	35.000000
75%	8011.500000	8.000000	15.000000	22.000000	28.000000	33.000000	38.000000
max	9934.000000	31.000000	37.000000	44.000000	46.000000	48.000000	49.000000

	Game	Date	A	B	C	D	E	F
0	6801	03/09/1968	3	14	18	22	25	33
1	6802	10/09/1968	13	20	23	29	32	34
2	6803	17/09/1968	8	12	26	27	34	38
3	6804	24/09/1968	1	14	17	26	35	39
4	6805	01/10/1968	1	7	8	9	11	30

	Game	Date	A	B	C	D	E	F
4042	3467	14/05/2022	9	16	17	21	26	29
4043	3468	17/05/2022	1	4	5	24	31	32
4044	3469	19/05/2022	1	8	18	25	29	30
4045	3470	21/05/2022	3	4	5	15	24	33
4046	3471	24/05/2022	6	10	13	20	23	35

	0	1	2	3	4	5
0	-0.582231	0.429455	0.130094	-0.139060	-0.573710	-0.276708
1	1.575345	1.431572	0.876674	0.887369	0.484404	-0.107497
2	0.496557	0.095416	1.324623	0.594104	0.786722	0.569346
3	-1.013746	0.429455	-0.019223	0.447471	0.937882	0.738557
4	-1.013746	-0.739681	-1.363068	-2.045287	-2.689939	-0.784341

	A	B	C	D	E	F
4039	1	14	15	17	28	37
4040	3	13	21	24	27	35
4041	17	19	21	23	25	34
4042	9	16	17	21	26	29
4043	1	4	5	24	31	32
4044	1	8	18	25	29	30
4045	3	4	5	15	24	33