<jupyter_start><jupyter_text>Model SetUp<jupyter_code>from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(8, input_dim = 784, activation = 'relu'))
model.add(Dense(16, activation = 'relu'))
model.add(Dense(10,activation = 'softmax'))   
model.summary()<jupyter_output>Model: "sequential_7"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_21 (Dense)            (None, 8)                 6280      
                                                                 
 dense_22 (Dense)            (None, 16)                144       
                                                                 
 dense_23 (Dense)            (None, 10)                170       
                                                                 
=================================================================
Total params: 6,594
Trainable params: 6,594
Non-trainable params: 0
_________________________________________________________________<jupyter_text>Training<jupyter_code>model.compile(loss= 'categorical_crossentropy',optimizer= 'adam', metrics=['accuracy'])
hist= model.fit(x= X_train, y= y_train, batch_size= 25, validation_data=(X_test, y_test), epochs= 40, verbose=1)<jupyter_output>Epoch 1/40
2400/2400 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9536 - val_loss: 0.2233 - val_accuracy: 0.9406
Epoch 2/40
2400/2400 [==============================] - 6s 2ms/step - loss: 0.1509 - accuracy: 0.9535 - val_loss: 0.2264 - val_accuracy: 0.9377
Epoch 3/40
2400/2400 [==============================] - 6s 2ms/step - loss: 0.1509 - accuracy: 0.9542 - val_loss: 0.2172 - val_accuracy: 0.9405
Epoch 4/40
2400/2400 [==============================] - 6s 2ms/step - loss: 0.1500 - accuracy: 0.9539 - val_loss: 0.2206 - val_accuracy: 0.9410
Epoch 5/40
2400/2400 [==============================] - 6s 3ms/step - loss: 0.1501 - accuracy: 0.9538 - val_loss: 0.2216 - val_accuracy: 0.9392
Epoch 6/40
2400/2400 [==============================] - 7s 3ms/step - loss: 0.1495 - accuracy: 0.9538 - val_loss: 0.2296 - val_accuracy: 0.9353
Epoch 7/40
2400/2400 [==============================] - 6s 3ms/step - loss: 0.1496 - accuracy: 0.9533 - val_loss: 0.2200 - val_accuracy:[...]
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '352515', 'license': 'MIT', 'n_tokens_mistral': 913, 'n_tokens_neox': 706, 'n_words': 157, 'path': 'MNIST.ipynb', 'repo_name': 'SidStark29/MNIST', 'size': '2415'}