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models.txt
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models.txt
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0001 - 0005 (only changes epochs and batch size)
-----------
def build_net(ds):
enc = text_encoder(ds, 2500)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=64,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(6, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"],
)
return model
0006
----
# Dropout layer (https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf, p. 1938)
def build_net(ds):
enc = text_encoder(ds, 2500)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=64,
mask_zero=True,
),
tf.keras.layer.Dropout(0.8),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layer.Dropout(0.7),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layer.Dropout(0.6),
tf.keras.layers.Dense(6, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"],
)
return model
0007
----
def build_net(ds):
enc = text_encoder(ds, 2500)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=64,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(6, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=["accuracy"],
)
return model
0008
----
def build_net(ds):
enc = text_encoder(ds, 2500)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=32,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(32)),
tf.keras.layers.Dense(32, activation="relu"),
tf.keras.layers.Dense(6, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=["accuracy"],
)
return model
0009
----
def build_net(ds):
enc = text_encoder(ds, 10_000)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=128,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(6, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=["accuracy"],
)
return model
0012
----
def build_net(ds):
hl1, hl2 = calculate_nodes_for(ds, 6)
print(f"Using \33[1m{hl1}:{hl2}\33[0m for hidden node counts")
enc = text_encoder(ds, None)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=hl1,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(hl1)),
tf.keras.layers.Dense(hl2, activation="relu"),
tf.keras.layers.Dense(6, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=["accuracy"],
)
return model
0013
----
0012 + weights
0014
----
0013 + learning rate = 2e-05
0015
----
0007 with new standardiser
1001
----
def build_net(ds):
enc = text_encoder(ds, 2_500)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=64,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(28, activation="sigmoid"),
]
)
model.compile(
loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-3),
metrics=["accuracy"],
)
return model
1002
----
class Model(tf.keras.Model):
def __init__(self, ds):
super().__init__()
self.encoder = text_encoder(ds, 2_500)
self.embedding = tf.keras.layers.Embedding(input_dim=len(self.encoder.get_vocabulary()), output_dim=64, mask_zero=True)
self.b1 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_state=True, return_sequences=True))
self.b2 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_state=True, return_sequences=True))
self.b3 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64))
self.d1 = tf.keras.layers.Dense(64, activation="relu")
self.d2 = tf.keras.layers.Dense(64, activation="relu")
self.d3 = tf.keras.layers.Dense(64, activation="relu")
self.out = tf.keras.layers.Dense(28, activation="sigmoid")
def call(self, inputs):
x = self.encoder(inputs)
x = self.embedding(x)
x = self.b1(x)
x = self.b2(x)
x = self.b3(x)
x = self.d1(x)
x = self.d2(x)
x = self.d3(x)
return self.out(x)
def build_net(ds):
model = Model(ds)
model.compile(
loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=["accuracy"],
)
return model
1003
----
-
1004
----
class Model(tf.keras.Model):
def __init__(self, ds):
super().__init__()
self.encoder = text_encoder(ds, 2_500)
self.embedding = tf.keras.layers.Embedding(input_dim=len(self.encoder.get_vocabulary()), output_dim=64, mask_zero=True)
self.b1 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_state=True, return_sequences=True))
self.b2 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_state=True, return_sequences=True))
self.b3 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64))
self.d1 = tf.keras.layers.Dense(64, activation="relu")
self.d2 = tf.keras.layers.Dense(64, activation="relu")
self.d3 = tf.keras.layers.Dense(64, activation="relu")
self.out = tf.keras.layers.Dense(28, activation="sigmoid")
def call(self, inputs):
x = self.encoder(inputs)
x = self.embedding(x)
x = self.b1(x)
x = self.b2(x)
x = self.b3(x)
x = self.d1(x)
x = self.d2(x)
x = self.d3(x)
return self.out(x)
def build_net(ds):
model = Model(ds)
model.compile(
loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=["accuracy"],
)
return model
1005
----
class Model(tf.keras.Model):
def __init__(self, ds):
super().__init__()
self.encoder = text_encoder(ds, 2_500)
self.embedding = tf.keras.layers.Embedding(input_dim=len(self.encoder.get_vocabulary()), output_dim=64, mask_zero=True)
self.bidirectional_1 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_state=True, return_sequences=True))
self.bidirectional_2 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_state=True, return_sequences=True))
self.bidirectional_3 = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64))
self.dense_1 = tf.keras.layers.Dense(64, activation="relu")
self.dense_2 = tf.keras.layers.Dense(64, activation="relu")
self.dense_3 = tf.keras.layers.Dense(64, activation="relu")
self.out = tf.keras.layers.Dense(28, activation="sigmoid")
def call(self, inputs):
x = self.encoder(inputs)
x = self.embedding(x)
x = self.bidirectional_1(x)
x = self.bidirectional_2(x)
x = self.bidirectional_3(x)
x = self.dense_1(x)
x = self.dense_2(x)
x = self.dense_3(x)
return self.out(x)
def build_net(ds):
model = Model(ds)
model.compile(
loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(1e-3),
metrics=["accuracy"],
)
return model
1006
----
def build_net(ds, n_samples):
hl1, hl2, outputs = calculate_nodes_for(ds, n_samples)
enc = text_encoder(ds, None)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=hl1,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(hl1)),
tf.keras.layers.Dense(hl2, activation="relu"),
tf.keras.layers.Dense(outputs, activation="sigmoid"),
]
)
model.compile(
loss="binary_crossentropy",
# optimizer=tf.keras.optimizers.Adam(1e-4),
optimizer="adam",
metrics=["accuracy"],
)
return model
2001 (90:54)
------------
* Huang's theorem (100%)
def build_net(ds):
outputs = 6
h1, h2 = calculate_hidden_nodes(len(ds), outputs)
print(f"Using {h1}:{h2} for hidden layers")
enc = text_encoder(ds, None)
model = tf.keras.Sequential(
[
enc,
tf.keras.layers.Embedding(
input_dim=len(enc.get_vocabulary()),
output_dim=h1,
mask_zero=True,
),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(h1)),
tf.keras.layers.Dense(h2, activation="relu"),
tf.keras.layers.Dense(outputs, activation="softmax"),
]
)
model.compile(
loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=[
"accuracy",
*[metrics.PrecisionForClass(x) for x in range(outputs)],
*[metrics.RecallForClass(x) for x in range(outputs)],
*[metrics.MccForClass(x) for x in range(outputs)],
],
)
return model
2002 (67:40)
------------
* 2001 w/ Huang's theorem (75%)
2003 (45:27)
------------
* 2001 w/ Huang's theorem (50%)
* 100 & 200 epoch variants
2004 (45:27)
------------
* 2003 w/ learning rate = 1e-4
2005 (45:27)
------------
* 2003 w/ dropout @ 0.5
2006 (45:27)
------------
* 2005 w/ learning rate = 1e-4
2007 (45:27)
------------
* 2005 w/ SGD optimiser, Nesterov momentum @ 0.9
* 50 and 100 epoch variants
* TODO: 150 epoch variant
2007R (45:27)
-------------
* 2007 w/ double RNN
2005R (45:27)
-------------
* 2005 w/ double RNN
Experiments
-----------
Model (64:64)
Model (L1:L2)
Model with learning rate 0.001
Model + SGD
Model + dropout
Batch size = 512
Epochs = 100
New standardiser