import numpy as np

D = np.array([[1,2,3],     # dataset
              [1,0,2],
              [0,1,4],
              [2,1,4]])

# Initialize weight matrix
np.random.seed(1)
w = 2*np.random.random((3,1))-1
print("Weight Matrix : ")
print(w)

#Forward Pass
for iteration in range(1):
    iLayer = D    
    oPer = np.dot(iLayer,w)         # Perceptron
    oLayer = 1/(1+np.exp(-oPer))      # Sigmoid
    
print("Input :")
print(D)
print("Output: ")
print(oLayer)

import numpy as np

D = np.array([[1,2,3],
              [1,0,2],
              [0,1,4],
              [2,1,4]])

# Initialize Weight Matrices
np.random.seed(1)
W = np.random.random((3,4))
print("W weight matrix:")
print(W)

print("V weight matrix:")
V = np.random.random((4,2))
print(V)

for iteration in range(1):
    iLayer = D
    
    hP = np.dot(iLayer,W)       # Hidden Layer
    hLayer = 1/(1+np.exp(-hP))
    
    oP = np.dot(hLayer,V)       # Output Layer
    oLayer = 1/(1+np.exp(-oP))
    
print("Input :")
print(training)
print("Predicted Output: ")
print(oLayer)

import numpy as np

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]])
label = np.array([[1,1,0,0]]).T

np.random.seed(1)
w = np.random.random((3,1))

for iteration in range(10):
    iLayer = D
    p = np.dot(iLayer,w)       # Perceptron
    print(p)
    oLayer = 1/(1+np.exp(-p))  # Sigmoid(x)
        
    MSE = 2*np.square(np.subtract(oLayer,label)).mean() # Mean Square Error
    print(MSE)
    
    der = p * (1-p) # dirivatives of sigmoid 
    grad = np.dot(iLayer.T, der *MSE)
    
    w += 0.00000000001*grad
    print(w)
    
print(oLayer)

import numpy as np

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]])
label = np.array([[1,1,0,0]]).T

np.random.seed(1)
w = np.random.random((3,4))
v = np.random.random((4,1))

for iteration in range(10):
    iLayer = D
    hP = np.dot(iLayer,w)       # Perceptron  
    hLayer = 1/(1+np.exp(-hP))  # Sigmoid(x)
    
    oP = np.dot(hLayer,v)       # Perceptron  
    oLayer = 1/(1+np.exp(-oP))  # Sigmoid(x)
    
    MSE = 2*np.square(np.subtract(oLayer,label)).mean() # Mean Square Error
    print(MSE)
    
    oDer = oP * (1-oP) # dirivatives of sigmoid 
    vGrad = np.dot(oLayer.T, oDer *MSE)
    v += 0.00000001*vGrad
    print(v)
    
    hDer = hP * (1-hP) # dirivatives of sigmoid 
    wGrad = np.dot(iLayer.T, hDer *v*oDer*MSE)
    w += 0.00000001*wGrad
    print(w)
    
print(oLayer)

testing = np.array([[1,2,3]])
for iteration in range(1):
    iLayer = testing
    p = np.dot(iLayer,w)       # Perceptron  
    oLayer = 1/(1+np.exp(-p))  # Sigmoid(x)
        
print(oLayer)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]])
label = np.array([[1,1,0,0]]).T


model = Sequential()
model.add(Dense(4, input_shape=(3,),activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
model.summary()

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # Configure the Model
model.fit(training, output, epochs=10, batch_size=250, verbose=1, validation_split=0.2) # Train the model for fix number of epochs

test_D = tf.constant([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]]).numpy()
test_label = tf.constant([[1,1,0,0]]).numpy().T

test_results = model.evaluate(test_, test_label, verbose=1)  # Evaluate the model
print(test_results)
print(f'Test results - Loss: {test_results[0]} - Accuracy: {test_results[1]}%')

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]])
label = np.array([[1,1,0,0]]).T


model = Sequential()
model.add(Dense(2, input_shape=(3,),activation='tanh'))   
#model.add(Dense(2,activation='tanh'))    
model.add(Dense(1,  activation='sigmoid'))
model.summary()


model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(training, output, epochs=10, batch_size=250, validation_data=(D,label))

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,Dense

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]])
label = np.array([[1,1,0,0]]).T

## Creating the layers
input_layer = Input(shape=(3,))
layer_1 = Dense(4, activation="relu")(input_layer)
layer_2 = Dense(4, activation="relu")(layer_1)
o_layer = Dense(4, activation="relu")(layer_2)

##Defining the model by specifying the input and output layers
model = Model(inputs=input_layer, outputs=o_layer)
model.summary()

## defining the optimiser and loss function
model.compile(optimizer='adam', loss='mse')

## training the model
model.fit(D, label,epochs=2, batch_size=128,validation_data=(D,label))

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,Dense

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]])
label = np.array([[1,1,0,0]]).T

## Creating the layers
input_layer = Input(shape=(3,))
layer_1 = Dense(4, activation="relu")(input_layer)
layer_2 = Dense(4, activation="relu")(layer_1)
layer_3 = Dense(4, activation="relu")(layer_2)
o1_layer= Dense(1, activation="linear")(layer_2)
o2_layer= Dense(1, activation="linear")(layer_3)

##Defining the model by specifying the input and output layers
model = Model(inputs=input_layer, outputs=[o1_layer,o2_layer])
model.summary()

## defining the optimiser and loss function
model.compile(optimizer='adam', loss='mse')

## training the model
model.fit(D, label,epochs=2, batch_size=128,validation_data=(D,label))

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

D = np.array([[4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4],
              [4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4],
              [4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4],
              [4,500,6],
              [4,550,5.5],
              [2,200,3.5],
              [2,250,4]
             ])
label = np.array([[1,0],[1,0],[0,1],[0,1],[1,0],[1,0],[0,1],[0,1],[1,0],[1,0],[0,1],[0,1],[1,0],[1,0],[0,1],[0,1]])


model = Sequential()
model.add(Dense(30, input_shape=(3,),activation='sigmoid'))   
model.add(Dense(20,activation='sigmoid'))    
model.add(Dense(2,  activation='softmax'))
model.summary()


model.compile(loss='mse', optimizer='adam', metrics=['accuracy']) #categorical_crossentropy
model.fit(D, label, epochs=100, batch_size=8, validation_split=0.2) #validation_data=(D,label))

Model: "sequential_21"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_57 (Dense)            (None, 30)                120       
                                                                 
 dense_58 (Dense)            (None, 20)                620       
                                                                 
 dense_59 (Dense)            (None, 2)                 42        
                                                                 
=================================================================
Total params: 782
Trainable params: 782
Non-trainable params: 0
_________________________________________________________________
Epoch 1/100
2/2 [==============================] - 1s 152ms/step - loss: 0.2548 - accuracy: 0.5000 - val_loss: 0.2523 - val_accuracy: 0.5000
Epoch 2/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2522 - accuracy: 0.5000 - val_loss: 0.2518 - val_accuracy: 0.5000
Epoch 3/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2517 - accuracy: 0.5000 - val_loss: 0.2512 - val_accuracy: 0.5000
Epoch 4/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2519 - accuracy: 0.5000 - val_loss: 0.2510 - val_accuracy: 0.5000
Epoch 5/100
2/2 [==============================] - 0s 24ms/step - loss: 0.2510 - accuracy: 0.5000 - val_loss: 0.2506 - val_accuracy: 0.5000
Epoch 6/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2505 - accuracy: 0.5000 - val_loss: 0.2498 - val_accuracy: 0.5000
Epoch 7/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2496 - accuracy: 0.5000 - val_loss: 0.2484 - val_accuracy: 0.5000
Epoch 8/100
2/2 [==============================] - 0s 24ms/step - loss: 0.2482 - accuracy: 0.5000 - val_loss: 0.2469 - val_accuracy: 0.5000
Epoch 9/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2467 - accuracy: 0.5000 - val_loss: 0.2460 - val_accuracy: 0.5000
Epoch 10/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2459 - accuracy: 0.5000 - val_loss: 0.2456 - val_accuracy: 0.5000
Epoch 11/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2456 - accuracy: 0.5000 - val_loss: 0.2454 - val_accuracy: 0.5000
Epoch 12/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2453 - accuracy: 0.5000 - val_loss: 0.2452 - val_accuracy: 0.5000
Epoch 13/100
2/2 [==============================] - 0s 24ms/step - loss: 0.2451 - accuracy: 0.5000 - val_loss: 0.2450 - val_accuracy: 0.5000
Epoch 14/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2449 - accuracy: 0.5000 - val_loss: 0.2447 - val_accuracy: 0.5000
Epoch 15/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2447 - accuracy: 0.5000 - val_loss: 0.2444 - val_accuracy: 1.0000
Epoch 16/100
2/2 [==============================] - 0s 24ms/step - loss: 0.2445 - accuracy: 1.0000 - val_loss: 0.2441 - val_accuracy: 1.0000
Epoch 17/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2439 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 1.0000
Epoch 18/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2438 - accuracy: 0.8333 - val_loss: 0.2437 - val_accuracy: 0.5000
Epoch 19/100
2/2 [==============================] - 0s 24ms/step - loss: 0.2435 - accuracy: 0.5000 - val_loss: 0.2435 - val_accuracy: 0.5000
Epoch 20/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2435 - accuracy: 0.5000 - val_loss: 0.2434 - val_accuracy: 0.5000
Epoch 21/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2434 - accuracy: 0.5000 - val_loss: 0.2432 - val_accuracy: 0.5000
Epoch 22/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2431 - accuracy: 0.5000 - val_loss: 0.2430 - val_accuracy: 0.5000
Epoch 23/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2430 - accuracy: 0.5000 - val_loss: 0.2428 - val_accuracy: 0.5000
Epoch 24/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2431 - accuracy: 0.5000 - val_loss: 0.2425 - val_accuracy: 0.5000
Epoch 25/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2426 - accuracy: 0.5000 - val_loss: 0.2422 - val_accuracy: 0.5000
Epoch 26/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2421 - accuracy: 0.5000 - val_loss: 0.2420 - val_accuracy: 0.5000
Epoch 27/100
2/2 [==============================] - 0s 32ms/step - loss: 0.2420 - accuracy: 0.5000 - val_loss: 0.2421 - val_accuracy: 0.5000
Epoch 28/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2420 - accuracy: 0.5000 - val_loss: 0.2418 - val_accuracy: 0.5000
Epoch 29/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2418 - accuracy: 0.5000 - val_loss: 0.2413 - val_accuracy: 0.5000
Epoch 30/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2418 - accuracy: 0.5000 - val_loss: 0.2409 - val_accuracy: 0.5000
Epoch 31/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2408 - accuracy: 0.5000 - val_loss: 0.2406 - val_accuracy: 0.5000
Epoch 32/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2405 - accuracy: 0.5000 - val_loss: 0.2402 - val_accuracy: 0.5000
Epoch 33/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2401 - accuracy: 0.5000 - val_loss: 0.2398 - val_accuracy: 0.5000
Epoch 34/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2399 - accuracy: 0.5000 - val_loss: 0.2394 - val_accuracy: 0.5000
Epoch 35/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2400 - accuracy: 0.5000 - val_loss: 0.2389 - val_accuracy: 0.5000
Epoch 36/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2396 - accuracy: 0.5000 - val_loss: 0.2385 - val_accuracy: 0.5000
Epoch 37/100
2/2 [==============================] - 0s 24ms/step - loss: 0.2384 - accuracy: 0.5833 - val_loss: 0.2381 - val_accuracy: 1.0000
Epoch 38/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2380 - accuracy: 1.0000 - val_loss: 0.2377 - val_accuracy: 1.0000
Epoch 39/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2377 - accuracy: 1.0000 - val_loss: 0.2373 - val_accuracy: 1.0000
Epoch 40/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2377 - accuracy: 1.0000 - val_loss: 0.2369 - val_accuracy: 1.0000
Epoch 41/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2368 - accuracy: 1.0000 - val_loss: 0.2366 - val_accuracy: 1.0000
Epoch 42/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2372 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 1.0000
Epoch 43/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2370 - accuracy: 1.0000 - val_loss: 0.2359 - val_accuracy: 1.0000
Epoch 44/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2359 - accuracy: 1.0000 - val_loss: 0.2356 - val_accuracy: 1.0000
Epoch 45/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2358 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 1.0000
Epoch 46/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2370 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 1.0000
Epoch 47/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2350 - accuracy: 1.0000 - val_loss: 0.2346 - val_accuracy: 1.0000
Epoch 48/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2346 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 1.0000
Epoch 49/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2343 - accuracy: 1.0000 - val_loss: 0.2342 - val_accuracy: 1.0000
Epoch 50/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2343 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 1.0000
Epoch 51/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2346 - accuracy: 0.8333 - val_loss: 0.2337 - val_accuracy: 1.0000
Epoch 52/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2335 - accuracy: 1.0000 - val_loss: 0.2331 - val_accuracy: 1.0000
Epoch 53/100

2/2 [==============================] - 0s 21ms/step - loss: 0.2330 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 1.0000
Epoch 54/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2328 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 1.0000
Epoch 55/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2328 - accuracy: 1.0000 - val_loss: 0.2327 - val_accuracy: 1.0000
Epoch 56/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2327 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 1.0000
Epoch 57/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2327 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 1.0000
Epoch 58/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2317 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 1.0000
Epoch 59/100
2/2 [==============================] - 0s 20ms/step - loss: 0.2318 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 1.0000
Epoch 60/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2308 - accuracy: 1.0000 - val_loss: 0.2303 - val_accuracy: 1.0000
Epoch 61/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2302 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 1.0000
Epoch 62/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2311 - accuracy: 1.0000 - val_loss: 0.2301 - val_accuracy: 1.0000
Epoch 63/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2300 - accuracy: 1.0000 - val_loss: 0.2294 - val_accuracy: 1.0000
Epoch 64/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2293 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 1.0000
Epoch 65/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2287 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 1.0000
Epoch 66/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2285 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 1.0000
Epoch 67/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2289 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 1.0000
Epoch 68/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2293 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 1.0000
Epoch 69/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2275 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 1.0000
Epoch 70/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2276 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 1.0000
Epoch 71/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2274 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 1.0000
Epoch 72/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2274 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 1.0000
Epoch 73/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2257 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 1.0000
Epoch 74/100
2/2 [==============================] - 0s 23ms/step - loss: 0.2255 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 1.0000
Epoch 75/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2253 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.7500
Epoch 76/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2260 - accuracy: 0.7500 - val_loss: 0.2252 - val_accuracy: 0.7500
Epoch 77/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2250 - accuracy: 0.7500 - val_loss: 0.2243 - val_accuracy: 1.0000
Epoch 78/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2241 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 1.0000
Epoch 79/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2247 - accuracy: 1.0000 - val_loss: 0.2235 - val_accuracy: 1.0000
Epoch 80/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2236 - accuracy: 1.0000 - val_loss: 0.2232 - val_accuracy: 1.0000
Epoch 81/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2231 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 1.0000
Epoch 82/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2226 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 1.0000
Epoch 83/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2218 - accuracy: 1.0000 - val_loss: 0.2213 - val_accuracy: 1.0000
Epoch 84/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2213 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 1.0000
Epoch 85/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2211 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 1.0000
Epoch 86/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2206 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 1.0000
Epoch 87/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2203 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 1.0000
Epoch 88/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2203 - accuracy: 1.0000 - val_loss: 0.2201 - val_accuracy: 1.0000
Epoch 89/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2203 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 1.0000
Epoch 90/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2195 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 1.0000
Epoch 91/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2187 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 1.0000
Epoch 92/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2182 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 1.0000
Epoch 93/100
2/2 [==============================] - 0s 20ms/step - loss: 0.2179 - accuracy: 1.0000 - val_loss: 0.2170 - val_accuracy: 1.0000
Epoch 94/100
2/2 [==============================] - 0s 20ms/step - loss: 0.2169 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 1.0000
Epoch 95/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2166 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 1.0000
Epoch 96/100
2/2 [==============================] - 0s 21ms/step - loss: 0.2172 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 1.0000
Epoch 97/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2156 - accuracy: 1.0000 - val_loss: 0.2149 - val_accuracy: 1.0000
Epoch 98/100
2/2 [==============================] - 0s 20ms/step - loss: 0.2159 - accuracy: 1.0000 - val_loss: 0.2148 - val_accuracy: 1.0000
Epoch 99/100
2/2 [==============================] - 0s 22ms/step - loss: 0.2147 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 1.0000
Epoch 100/100
2/2 [==============================] - 0s 19ms/step - loss: 0.2141 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 1.0000

<keras.callbacks.History at 0x20962762ca0>

test_x = [[2,300,3],
          [4,600,6]]
test_y = [[0,1],[1,0]]
#model.evaluate(test_x,test_y)
prediction = model.predict(test_x)
print(prediction)

1/1 [==============================] - 0s 59ms/step
[[0.5074549 0.4925451]
 [0.5417739 0.4582261]]