0cfe82c6beb9a28a5ff7da81b86fa0e93c388f14,tasks/save_reload_model.py,,,#,10

Before Change

// simple TCN model.
max_len = 100
max_features = 50
i = Input(shape=(max_len,))
x = Embedding(max_features, 16)(i)
x = TCN(nb_filters=12,
        dropout_rate=0.5,  // with dropout here.
        kernel_size=6,
        dilations=[1, 2, 4])(x)
x = Dropout(0.5)(x)  // and dropout here.
x = Dense(1, activation="sigmoid")(x)

model = Model(inputs=[i], outputs=[x])

if os.path.exists("tcn.npz"):
    // Load checkpoint if file exists.
    w = np.load("tcn.npz", allow_pickle=True)["w"]
    print("Model reloaded.")
    model.set_weights(w.tolist())
else:
    // Save the checkpoint.
    w = np.array(model.get_weights())
    np.savez_compressed(file="tcn.npz", w=w, allow_pickle=True)
    print("First time.")

// Make inference.
// The value for [First time] and [Model reloaded] should be the same. Run the script twice!
inputs = np.ones(shape=(1, 100))

After Change

                           Dense(units=1, activation="sigmoid")])

// get model as json string and save to file
model_as_json = model.to_json()
with open(r"model.json", "w") as json_file:
    json_file.write(model_as_json)
// save weights to file (for this format, need h5py installed)
model.save_weights("weights.h5")

// Make inference.
inputs = np.ones(shape=(1, 100))

Instances