Fix output activation is none error
Browse files- chatbot_constructor.py +5 -10
chatbot_constructor.py
CHANGED
@@ -14,14 +14,6 @@ import keras
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os.mkdir("cache")
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class ValueConstraint(Constraint):
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def __init__(self, min_value: float = -1, max_value: float = 1):
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self.min_value = min_value
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self.max_value = max_value
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def __call__(self, w):
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return K.clip(w, self.min_value, self.max_value)
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def todset(text: str):
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lines = [x.rstrip("\n").lower().split("→") for x in text.split("\n")]
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lines = [(x[0].replace("\\n", "\n"), x[1].replace("\\n", "\n")) for x in lines]
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@@ -79,7 +71,7 @@ def train(message: str = "", regularization: float = 0.0001, dropout: float = 0.
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concat1_layer = Concatenate()([flatten_layer, attn_flatten_layer, conv1_flatten_layer, conv2_flatten_layer, conv3_flatten_layer])
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dropout2_layer = Dropout(dropout)(concat1_layer)
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dense1_layer = Dense(1024, activation="linear", kernel_regularizer=L1(regularization))(dropout2_layer)
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prelu1_layer = PReLU(
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dropout3_layer = Dropout(dropout)(prelu1_layer)
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dense2_layer = Dense(512, activation="relu", kernel_regularizer=L1(regularization))(dropout3_layer)
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dropout4_layer = Dropout(dropout)(dense2_layer)
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@@ -87,7 +79,10 @@ def train(message: str = "", regularization: float = 0.0001, dropout: float = 0.
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dropout5_layer = Dropout(dropout)(dense3_layer)
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dense4_layer = Dense(256, activation="relu", kernel_regularizer=L1(regularization))(dropout5_layer)
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concat2_layer = Concatenate()([dense4_layer, prelu1_layer, attn_flatten_layer, conv1_flatten_layer])
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model = Model(inputs=input_layer, outputs=dense4_layer)
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X = []
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os.mkdir("cache")
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def todset(text: str):
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lines = [x.rstrip("\n").lower().split("→") for x in text.split("\n")]
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lines = [(x[0].replace("\\n", "\n"), x[1].replace("\\n", "\n")) for x in lines]
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concat1_layer = Concatenate()([flatten_layer, attn_flatten_layer, conv1_flatten_layer, conv2_flatten_layer, conv3_flatten_layer])
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dropout2_layer = Dropout(dropout)(concat1_layer)
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dense1_layer = Dense(1024, activation="linear", kernel_regularizer=L1(regularization))(dropout2_layer)
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prelu1_layer = PReLU()(dense1_layer)
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dropout3_layer = Dropout(dropout)(prelu1_layer)
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dense2_layer = Dense(512, activation="relu", kernel_regularizer=L1(regularization))(dropout3_layer)
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dropout4_layer = Dropout(dropout)(dense2_layer)
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dropout5_layer = Dropout(dropout)(dense3_layer)
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dense4_layer = Dense(256, activation="relu", kernel_regularizer=L1(regularization))(dropout5_layer)
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concat2_layer = Concatenate()([dense4_layer, prelu1_layer, attn_flatten_layer, conv1_flatten_layer])
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if end_activation is not None:
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dense4_layer = Dense(resps_len, activation=end_activation, kernel_regularizer=L1(regularization))(concat2_layer)
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else:
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dense4_layer = Dense(resps_len, activation="softmax", kernel_regularizer=L1(regularization))(concat2_layer)
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model = Model(inputs=input_layer, outputs=dense4_layer)
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X = []
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