print("x_train shape: {}".format(x_train.shape))
print("y_train shape: {}".format(y_train.shape))
#print("x_train: {}".format(x_train[1:]))
#print("y_train: {}".format(y_train[1:]))
#y_map
#from keras.callbacks import ModelCheckpoint
#filepath="weights.best.hdf5"
#checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
# ## Create the neural network definition
# In[11]:
classifier = AmazonKerasClassifier()
classifier.add_conv_layer(img_resize)
classifier.add_flatten_layer()
classifier.add_ann_layer(len(y_map))
train_losses, val_losses, fbeta_score = classifier.train_model(
x_train,
y_train,
epochs,
batch_size,
validation_split_size=validation_split_size)
classifier.load_weight()
print("Weights loaded")
# ## Monitor the results
def build_classifier(output_layers):
classifier = AmazonKerasClassifier()
classifier.add_conv_layer(img_resize)
classifier.add_flatten_layer()
classifier.add_ann_layer(output_layers)
return classifier
# <codecell>
validation_split_size = 0.2
batch_size = 128
# <markdowncell>
# ## Define and Train model
#
# Here we define the model and begin training.
#
# Note that we have created a learning rate annealing schedule with a series of learning rates as defined in the array `learn_rates` and corresponding number of epochs for each `epochs_arr`. Feel free to change these values if you like or just use the defaults.
# <codecell>
classifier = AmazonKerasClassifier()
classifier.add_conv_layer(img_resize)
classifier.add_flatten_layer()
classifier.add_ann_layer(len(y_map))
train_losses, val_losses = [], []
epochs_arr = [10, 5, 5]
learn_rates = [0.001, 0.0001, 0.00001]
for learn_rate, epochs in zip(learn_rates, epochs_arr):
tmp_train_losses, tmp_val_losses, fbeta_score = classifier.train_model(
x_train,
y_train,
learn_rate,
epochs,
batch_size,
validation_split_size=validation_split_size,
x_valid = HDF5Matrix(h5_train_file, "x_train", start=N_split, end=N_train)
y_valid = HDF5Matrix(h5_train_file, "y_train", start=N_split, end=N_train)
y = HDF5Matrix(h5_train_file, "y_train")
counts = np.zeros((17))
for i in range(len(y)):
counts = counts + y[i]
w_sample = dict(zip([x for x in range(len(counts))], 1000 / counts))
#np.vectorize(lambda x: y_map.get(x,0))(y_train)
# <codecell>
# <markdowncell>
# ## Define and Train model
# Here we define the model and begin training.
# Note that we have created a learning rate annealing schedule with a series of learning rates as defined in the array `learn_rates` and corresponding number of epochs for each `epochs_arr`. Feel free to change these values if you like or just use the defaults.
classifier = AmazonKerasClassifier()
#classifier.load_model(model_filepath+".json") # load model
classifier.add_conv_layer(img_resize, img_channels)
classifier.add_flatten_layer()
classifier.add_ann_layer(len(y_map))
classifier.summary()
classifier.save_model(model_filepath + ".json")
checkpoint = ModelCheckpoint(model_filepath + ".hdf5",
monitor='val_acc',
verbose=1,
save_best_only=True)
train_losses, val_losses = [], []
epochs_arr = [15, 7, 7]
learn_rates = [0.001, 0.0001, 0.00001]
# <codecell>
batch_size = 64
epochs_arr = [35, 15, 5]
learn_rates = [0.002, 0.0002, 0.00002]
# <markdowncell>
# ## Define and Train model
#
# Here we define the model and begin training.
# <codecell>
classifier = AmazonKerasClassifier(preprocessor)
classifier.add_conv_layer()
classifier.add_flatten_layer()
classifier.add_ann_layer(len(preprocessor.y_map))
train_losses, val_losses = [], []
for learn_rate, epochs in zip(learn_rates, epochs_arr):
tmp_train_losses, tmp_val_losses, fbeta_score = classifier.train_model(learn_rate, epochs, batch_size,
train_callbacks=[checkpoint, fbeta, csv])
train_losses += tmp_train_losses
val_losses += tmp_val_losses
print(fbeta.fbeta)
# <markdowncell>
validation_split_size = 0.2
batch_size = 128
x_train, y_train, y_map = data_helper.preprocess_train_data(train_jpeg_dir, train_csv_file, img_resize)
# Free up all available memory space after this heavy operation
gc.collect();
print("x_train shape: {}".format(x_train.shape))
print("y_train shape: {}".format(y_train.shape))
from tensorflow.contrib.keras.api.keras.callbacks import ModelCheckpoint
filepath="weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True)
classifier = AmazonKerasClassifier()
classifier.add_conv_layer(img_resize)
classifier.add_flatten_layer()
classifier.add_ann_layer(len(y_map))
train_losses, val_losses = [], []
epochs_arr = [20, 5, 5]
learn_rates = [0.001, 0.0001, 0.00001]
for learn_rate, epochs in zip(learn_rates, epochs_arr):
tmp_train_losses, tmp_val_losses, fbeta_score = classifier.train_model(x_train, y_train, learn_rate, epochs,
batch_size, validation_split_size=validation_split_size,
train_callbacks=[checkpoint])
train_losses += tmp_train_losses
val_losses += tmp_val_losses
classifier.load_weights("weights.best.hdf5")
print("Weights loaded")