<jupyter_start><jupyter_text>Model test 1<jupyter_code>input_layer = Input(shape=(7, 1, 4))
conv_1 = Conv2D(filters=10, kernel_size=3, padding='same', activation='relu')(input_layer)
bn_1 = BatchNormalization()(conv_1)
conv_2 = Conv2D(filters=10, kernel_size=3, padding='same', activation='relu')(bn_1)
drop_1 = Dropout(0.25)(conv_2)

flatten = Flatten()(drop_1)
predictions = Dense(4, activation='softmax')(flatten)

model = Model(input_layer, predictions)

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

print(model.summary())

batch_size = 10000
epochs = 200

model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_validate, y_validate))<jupyter_output>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         (None, 7, 1, 4)           0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 7, 1, 10)          370       
_________________________________________________________________
batch_normalization_1 (Batch (None, 7, 1, 10)          40        
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 7, 1, 10)          910       
_________________________________________________________________
dropout_2 (Dropout)          (None, 7, 1, 10)          0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 70)                0         
_________________________________________________________________
dense_4 (D[...]