history = model.fit(
X=Xs["train"],
y=ys["train"],
batch_size=batch_size,
nb_epoch=epochs,
verbose=1,
validation_data=(Xs["val"], ys["val"]),
shuffle=True,
show_accuracy=True,
)
model.layers.pop()
model.compile(optimizer=Adam(lr=lr), loss="binary_crossentropy")
train_pred = model.predict(X=Xs["train"],
batch_size=batch_size,
verbose=0)
val_pred = model.predict(X=Xs["val"], batch_size=batch_size, verbose=0)
cou = 0
with open(args.last_layer_file, "w") as layer_file:
for vec in train_pred:
layer_file.write(",".join([str(i) for i in vec]))
layer_file.write("\n")
cou += 1
for vec in val_pred:
layer_file.write(",".join([str(i) for i in vec]))
layer_file.write("\n")
cou += 1
print(cou)
with open(args.last_layer_file + ".labels", "w") as label_file:
print("Model built")
history = model.fit(
X=Xs["train"],
y=ys["train"],
batch_size=batch_size,
nb_epoch=epochs,
verbose=1,
validation_data=(Xs["val"], ys["val"]),
shuffle=True,
show_accuracy=True,
)
model.layers.pop()
model.compile(optimizer=Adam(lr=lr), loss="binary_crossentropy")
train_pred = model.predict(X=Xs["train"], batch_size=batch_size, verbose=0)
val_pred = model.predict(X=Xs["val"], batch_size=batch_size, verbose=0)
cou = 0
with open(args.last_layer_file, "w") as layer_file:
for vec in train_pred:
layer_file.write(",".join([str(i) for i in vec]))
layer_file.write("\n")
cou += 1
for vec in val_pred:
layer_file.write(",".join([str(i) for i in vec]))
layer_file.write("\n")
cou += 1
print(cou)
with open(args.last_layer_file+".labels", "w") as label_file:
for value in ys["train"]:
