from tensorflow.keras.models import load_model
"""
#create model
model = Sequential([
Dense(units=16, input_shape=(1,), activation='relu'),
Dense(units=32, activation='relu'),
Dense(units=2, activation='softmax')
])
model.summary()
#training & validation
model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x=scaled_train_samples, y=train_lables, validation_split=0.1, batch_size=10, epochs=30, shuffle=True, verbose=2)
#save model
model.save('models/model1.h5')
"""
#load model
model = load_model('models/model1.h5')
model.summary()
print( model.get_weights())
print(model.optimizer) ------------------------------
#logs
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 16) 32
_________________________________________________________________
dense_1 (Dense) (None, 32) 544
_________________________________________________________________
dense_2 (Dense) (None, 2) 66
=================================================================
Total params: 642
Trainable params: 642
Non-trainable params: 0
_________________________________________________________________
[array([[ 0.26625147, -0.30418316, -0.13632423, -0.4079098 , -0.08543348,
0.5374129 , 0.4584436 , -0.28098437, -0.52589685, -0.4887119 ,
0.01660078, -0.10933313, -0.04969841, -0.10267085, 0.7305957 ,
0.38774747]], dtype=float32), array([-0.09283859, 0. , 0. , 0. , 0. ,
-0.09529305, -0.13439798, 0. , 0. , 0. ,
0.20418786, 0. , 0. , 0. , -0.13027988,
-0.1161219 ], dtype=float32), array([[ 0.26004615, -0.31933463, 0.21335247, 0.06417851, -0.12653175,
-0.40698838, -0.2899054 , -0.3321916 , -0.51734823, 0.2656369 ,
0.25500387, -0.23773687, 0.09429104, -0.5213961 , 0.5374189 ,
0.47588807, 0.15414259, -0.26182094, -0.06989337, 0.16001046,
0.05894524, 0.5102477 , 0.10094702, -0.20431465, -0.03044271,
-0.25523925, -0.40259373, 0.07096949, -0.3661572 , 0.40184933,
-0.20758483, 0.01585205],
[ 0.2500454 , -0.31888163, 0.12653747, -0.34026104, 0.207423 ,
0.22553 , -0.21539871, 0.10445303, -0.30555665, -0.28052825,
0.0283457 , 0.21818611, 0.24136344, -0.13501498, 0.01192671,
-0.10412639, 0.2694293 , -0.22594748, -0.06913146, -0.30933705,
0.00475213, -0.09345153, 0.20352694, -0.163133 , 0.13384211,
-0.19982849, -0.28636962, -0.31544378, 0.1858199 , 0.1552572 ,
0.01639351, 0.2738203 ],
...
[ 0.50497067, -0.30627 ],
[ 0.3936801 , -0.17796172],
[-0.65509355, 0.02540744],
[-0.10199237, -0.04108612],
[ 0.7816102 , -0.721409 ]], dtype=float32), array([ 0.09544881, -0.09544882], dtype=float32)]
<tensorflow.python.keras.optimizer_v2.adam.Adam object at 0x00000145A451F8E0>
loaded model prediction on test data set is still accurate
reference:
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