我们从Python开源项目中，提取了以下24个代码示例，用于说明如何使用tflearn.dropout()。
def buildModel(layers, hidden_nodes, maxlen, char_idx, dropout = False): g = tflearn.input_data([None, maxlen, len(char_idx)]) for n in range(layers-1): g = tflearn.lstm(g, hidden_nodes, return_seq=True) if dropout: g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, hidden_nodes) if dropout: g = tflearn.dropout(g, 0.5) g = tflearn.fully_connected(g, len(char_idx), activation='softmax') g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) return g # inputs: # data - textfile # outputs: # model - a TFlearn model file # dictionary - char_idx pickle # params: # history - max length of sequence to feed into neural net # layers - number of hidden layers of the network # epochs - how many epochs to run # hidden_nodes - how many nodes per hidden layer
def convolve_me(self, hyp, pd): network = input_data(shape=[None, pd.max_sequence], name='input') network = tflearn.embedding(network, input_dim=pd.vocab_size, output_dim=pd.emb_size, name="embedding") branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') return network
def build(embedding_size=(400000, 50), train_embedding=False, hidden_dims=128, learning_rate=0.001): net = tflearn.input_data([None, 200]) net = tflearn.embedding(net, input_dim=embedding_size[0], output_dim=embedding_size[1], trainable=train_embedding, name='EmbeddingLayer') net = tflearn.lstm(net, hidden_dims, return_seq=True) net = tflearn.dropout(net, 0.5) net = tflearn.lstm(net, hidden_dims, return_seq=True) net = tflearn.dropout(net, 0.5) net = tflearn.lstm(net, hidden_dims, return_seq=True) net = tflearn.dropout(net, 0.5) net = tflearn.lstm(net, hidden_dims) net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='adam', learning_rate=learning_rate, loss='categorical_crossentropy') return net
def make_network(look_back, batch_size): """ Declare the layer types and sizes """ # create deep neural network with LSTM and fully connected layers net = tfl.input_data(shape=[None, look_back, 1], name='input') net = tfl.lstm(net, 32, activation='tanh', weights_init='xavier', name='LSTM1') net = tfl.fully_connected(net, 20, activation='relu', name='FC1') # net = tfl.dropout(net, 0.5) net = tfl.fully_connected(net, 40, activation='relu', name='FC2') # net = tfl.dropout(net, 0.5) net = tfl.fully_connected(net, 1, activation='linear', name='Linear') net = tfl.regression(net, batch_size=batch_size, optimizer='adam', learning_rate=0.005, loss='mean_square', name='target') col = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) for x in col: tf.add_to_collection(tf.GraphKeys.VARIABLES, x) return net
def build_model(maxlen, char_idx, checkpoint_path): g = tflearn.input_data([None, maxlen, len(char_idx)]) g = tflearn.lstm(g, 512, return_seq=True) g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, 512, return_seq=True) g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, 512) g = tflearn.dropout(g, 0.5) g = tflearn.fully_connected(g, len(char_idx), activation='softmax') g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) return tflearn.SequenceGenerator(g, dictionary=char_idx, seq_maxlen=maxlen, clip_gradients=5.0, checkpoint_path=checkpoint_path)
def big_boy(self, hyp, pd): restore = True net = tflearn.input_data([None, pd.max_sequence], dtype=tf.float32) net = tflearn.embedding(net, input_dim=pd.vocab_size, output_dim=pd.emb_size, name="embedding", restore=restore) net = tflearn.lstm(net, 512, dropout=hyp.lstm.dropout, weights_init='uniform_scaling', dynamic=True, name="lstm", restore=restore) net = tflearn.fully_connected(net, 128, activation='sigmoid', regularizer='L2', weight_decay=hyp.middle.weight_decay, weights_init='uniform_scaling', name="middle", restore=restore) net = tflearn.dropout(net, hyp.dropout.dropout, name="dropout") net = tflearn.fully_connected(net, 2, activation='softmax', regularizer='L2', weight_decay=hyp.output.weight_decay, weights_init='uniform_scaling', name="output", restore=restore) net = tflearn.regression(net, optimizer='adam', learning_rate=hyp.regression.learning_rate, loss='categorical_crossentropy') return net
def bidirectional(self, hyp, pd): restore = True net = tflearn.input_data([None, pd.max_sequence], dtype=tf.float32) net = tflearn.embedding(net, input_dim=pd.vocab_size, output_dim=pd.emb_size, name="embedding", restore=restore) net = bidirectional_rnn(net, BasicLSTMCell(256), BasicLSTMCell(256), dynamic=True) net = tflearn.fully_connected(net, 128, activation='sigmoid', regularizer='L2', weight_decay=hyp.middle.weight_decay, name="middle", restore=restore) net = tflearn.dropout(net, hyp.dropout.dropout, name="dropout") net = tflearn.fully_connected(net, 2, activation='softmax', regularizer='L2', weight_decay=hyp.output.weight_decay, name="output", restore=restore) net = tflearn.regression(net, optimizer='adam', learning_rate=hyp.regression.learning_rate, loss='categorical_crossentropy') return net
def handle_speaker_rec_test_intent(self, message): speakers = data.get_speakers() number_classes=len(speakers) #print("speakers",speakers) #batch=data.wave_batch_generator(batch_size=1000, source=data.Source.DIGIT_WAVES, target=data.Target.speaker) #X,Y=next(batch) # Classification #tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5) net = tflearn.input_data(shape=[None, 8192]) #Two wave chunks net = tflearn.fully_connected(net, 64) net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, number_classes, activation='softmax') net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy') model = tflearn.DNN(net) #model.fit(X, Y, n_epoch=100, show_metric=True, snapshot_step=100) CWD_PATH = os.path.dirname(__file__) path_to_model = os.path.join(CWD_PATH, 'model', 'model.tfl') model.load(path_to_model) demo_file = "8_Vicki_260.wav" #demo_file = "8_Bruce_260.wav" demo=data.load_wav_file(data.path + demo_file) result=model.predict([demo]) result=data.one_hot_to_item(result,speakers) if result == "Vicki": self.speak("I am confident I'm speaking to %s"%(result)) # ~ 97% correct else: self.speak("I'm sorry I don't recognize your voice")
def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False): ''' Model - deep, i.e. two-layer fully connected network model ''' cc_input_var = {} cc_embed_var = {} flat_vars = [] if self.verbose: print ("--> deep model: %s categories, %d continuous" % (len(self.categorical_columns), n_inputs)) for cc, cc_size in self.categorical_columns.items(): cc_input_var[cc] = tflearn.input_data(shape=[None, 1], name="%s_in" % cc, dtype=tf.int32) # embedding layers only work on CPU! No GPU implementation in tensorflow, yet! cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(cc_input_var[cc], cc_size, 8, name="deep_%s_embed" % cc) if self.verbose: print (" %s_embed = %s" % (cc, cc_embed_var[cc])) flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc)) network = tf.concat([wide_inputs] + flat_vars, 1, name="deep_concat") for k in range(len(n_nodes)): network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1)) if use_dropout: network = tflearn.dropout(network, 0.5, name="deep_dropout%d" % (k+1)) if self.verbose: print ("Deep model network before output %s" % network) network = tflearn.fully_connected(network, 1, activation="linear", name="deep_fc_output", bias=False) network = tf.reshape(network, [-1, 1]) # so that accuracy is binary_accuracy if self.verbose: print ("Deep model network %s" % network) return network
def make_core_network(network): network = tflearn.reshape(network, [-1, 28, 28, 1], name="reshape") network = conv_2d(network, 32, 3, activation='relu', regularizer="L2") network = max_pool_2d(network, 2) network = local_response_normalization(network) network = conv_2d(network, 64, 3, activation='relu', regularizer="L2") network = max_pool_2d(network, 2) network = local_response_normalization(network) network = fully_connected(network, 128, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 256, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 10, activation='softmax') return network
def make_core_network(network): dense1 = tflearn.fully_connected(network, 64, activation='tanh', regularizer='L2', weight_decay=0.001, name="dense1") dropout1 = tflearn.dropout(dense1, 0.8) dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh', regularizer='L2', weight_decay=0.001, name="dense2") dropout2 = tflearn.dropout(dense2, 0.8) softmax = tflearn.fully_connected(dropout2, 10, activation='softmax', name="softmax") return softmax
def vgg16(input, num_class): x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1') x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1') x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1') x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2') x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1') x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2') x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5') x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6') x = tflearn.dropout(x, 0.5, name='dropout1') x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7') x = tflearn.dropout(x, 0.5, name='dropout2') x = tflearn.fully_connected(x, num_class, activation='softmax', scope='fc8', restore=False) return x
def test_sequencegenerator(self): with tf.Graph().as_default(): text = "123456789101234567891012345678910123456789101234567891012345678910" maxlen = 5 X, Y, char_idx = \ tflearn.data_utils.string_to_semi_redundant_sequences(text, seq_maxlen=maxlen, redun_step=3) g = tflearn.input_data(shape=[None, maxlen, len(char_idx)]) g = tflearn.lstm(g, 32) g = tflearn.dropout(g, 0.5) g = tflearn.fully_connected(g, len(char_idx), activation='softmax') g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.1) m = tflearn.SequenceGenerator(g, dictionary=char_idx, seq_maxlen=maxlen, clip_gradients=5.0) m.fit(X, Y, validation_set=0.1, n_epoch=100, snapshot_epoch=False) res = m.generate(10, temperature=.5, seq_seed="12345") #self.assertEqual(res, "123456789101234", "SequenceGenerator test failed! Generated sequence: " + res + " expected '123456789101234'") # Testing save method m.save("test_seqgen.tflearn") self.assertTrue(os.path.exists("test_seqgen.tflearn.index")) # Testing load method m.load("test_seqgen.tflearn") res = m.generate(10, temperature=.5, seq_seed="12345") # TODO: Fix test #self.assertEqual(res, "123456789101234", "SequenceGenerator test failed after loading model! Generated sequence: " + res + " expected '123456789101234'")
def CharacterLSTM_Run(seed, dictionary, model, output, steps = 600, layers = 3, hidden_nodes = 512, history = 25, temperature = 0.5, dropout = False): char_idx_file = dictionary maxlen = history char_idx = None if os.path.isfile(char_idx_file): print('Loading previous char_idx') char_idx = pickle.load(open(char_idx_file, 'rb')) tf.reset_default_graph() g = buildModel(layers, hidden_nodes, maxlen, char_idx, dropout) ''' g = tflearn.input_data([None, maxlen, len(char_idx)]) for n in range(layers-1): g = tflearn.lstm(g, hidden_nodes, return_seq=True) if dropout: g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, hidden_nodes) if dropout: g = tflearn.dropout(g, 0.5) g = tflearn.fully_connected(g, len(char_idx), activation='softmax') g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) ''' m = tflearn.SequenceGenerator(g, dictionary=char_idx, seq_maxlen=maxlen, clip_gradients=5.0) #, checkpoint_path='model_history_gen') m.load(model) #seed = random_sequence_from_textfile(data, maxlen) print('seed='+seed) print('len=' + str(len(seed))) result = m.generate(steps, temperature=temperature, seq_seed=seed[:history]) print (result) return result
def build(embedding_size=(400000, 50), train_embedding=False, hidden_dims=128, learning_rate=0.001): net = tflearn.input_data([None, 200]) net = tflearn.embedding(net, input_dim=embedding_size[0], output_dim=embedding_size[1], trainable=train_embedding, name='EmbeddingLayer') net = tflearn.lstm(net, hidden_dims) net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='adam', learning_rate=learning_rate, loss='categorical_crossentropy') return net
def generate_net(embedding): net = tflearn.input_data([None, 200]) net = tflearn.embedding(net, input_dim=300000, output_dim=128) net = tflearn.lstm(net, 128) net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy') return net
def initialize_model(self): char_idx_file = 'char_idx.pickle' maxlen = 25 char_idx = None if os.path.isfile(char_idx_file): print('Loading previous char_idx') char_idx = pickle.load(open(char_idx_file, 'rb')) X, Y, char_idx = textfile_to_semi_redundant_sequences(path, seq_maxlen=maxlen, redun_step=3, pre_defined_char_idx=char_idx) g = tflearn.input_data([None, maxlen, len(char_idx)]) g = tflearn.lstm(g, 512, return_seq=True) g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, 512, return_seq=True) g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, 512) g = tflearn.dropout(g, 0.5) g = tflearn.fully_connected(g, len(char_idx), activation='softmax') g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.01) m = tflearn.SequenceGenerator(g, dictionary=char_idx, seq_maxlen=maxlen, clip_gradients=5.0, checkpoint_path='model_tweets') # Load the model m.load("model.tfl") self.__text_model = m
def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False): ''' Model - deep, i.e. two-layer fully connected network model ''' cc_input_var = {} cc_embed_var = {} flat_vars = [] if self.verbose: print ("--> deep model: %s categories, %d continuous" % (len(self.categorical_columns), n_inputs)) for cc, cc_size in self.categorical_columns.items(): cc_input_var[cc] = tflearn.input_data(shape=[None, 1], name="%s_in" % cc, dtype=tf.int32) # embedding layers only work on CPU! No GPU implementation in tensorflow, yet! cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(cc_input_var[cc], cc_size, 8, name="deep_%s_embed" % cc) if self.verbose: print (" %s_embed = %s" % (cc, cc_embed_var[cc])) flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc)) network = tf.concat(1, [wide_inputs] + flat_vars, name="deep_concat") for k in range(len(n_nodes)): network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1)) if use_dropout: network = tflearn.dropout(network, 0.5, name="deep_dropout%d" % (k+1)) if self.verbose: print ("Deep model network before output %s" % network) network = tflearn.fully_connected(network, 1, activation="linear", name="deep_fc_output", bias=False) network = tf.reshape(network, [-1, 1]) # so that accuracy is binary_accuracy if self.verbose: print ("Deep model network %s" % network) return network
def example_net(x): network = tflearn.conv_2d(x, 32, 3, activation='relu') network = tflearn.max_pool_2d(network, 2) network = tflearn.conv_2d(network, 64, 3, activation='relu') network = tflearn.conv_2d(network, 64, 3, activation='relu') network = tflearn.max_pool_2d(network, 2) network = tflearn.fully_connected(network, 512, activation='relu') network = tflearn.dropout(network, 0.5) network = tflearn.fully_connected(network, 3, activation='softmax') return network
def spectacular_bid(self, hyp, pd): net = tflearn.input_data( [None, pd.max_sequence] ,dtype=tf.float32 ) net = tflearn.embedding( net, input_dim=pd.vocab_size, output_dim=pd.emb_size, name="embedding" ) net = tflearn.lstm( net, 750, dynamic=True, name="lstm_1", return_seq=True, dropout=hyp.lstm.dropout ) net = tflearn.dropout(net, hyp.dropout.dropout, name="dropout") net = tflearn.lstm( net, 750, name="lstm_2", return_seq=False ) net = tflearn.fully_connected( net, 2, activation='softmax', name="output", regularizer='L2', weight_decay=hyp.output.weight_decay ) net = tflearn.regression( net, optimizer='adam', learning_rate=hyp.regression.learning_rate, loss='categorical_crossentropy' ) return net
def test_core_layers(self): X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] Y_nand = [[1.], [1.], [1.], [0.]] Y_or = [[0.], [1.], [1.], [1.]] # Graph definition with tf.Graph().as_default(): # Building a network with 2 optimizers g = tflearn.input_data(shape=[None, 2]) # Nand operator definition g_nand = tflearn.fully_connected(g, 32, activation='linear') g_nand = tflearn.fully_connected(g_nand, 32, activation='linear') g_nand = tflearn.fully_connected(g_nand, 1, activation='sigmoid') g_nand = tflearn.regression(g_nand, optimizer='sgd', learning_rate=2., loss='binary_crossentropy') # Or operator definition g_or = tflearn.fully_connected(g, 32, activation='linear') g_or = tflearn.fully_connected(g_or, 32, activation='linear') g_or = tflearn.fully_connected(g_or, 1, activation='sigmoid') g_or = tflearn.regression(g_or, optimizer='sgd', learning_rate=2., loss='binary_crossentropy') # XOR merging Nand and Or operators g_xor = tflearn.merge([g_nand, g_or], mode='elemwise_mul') # Training m = tflearn.DNN(g_xor) m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False) # Testing self.assertLess(m.predict([[0., 0.]])[0][0], 0.01) self.assertGreater(m.predict([[0., 1.]])[0][0], 0.9) self.assertGreater(m.predict([[1., 0.]])[0][0], 0.9) self.assertLess(m.predict([[1., 1.]])[0][0], 0.01) # Bulk Tests with tf.Graph().as_default(): net = tflearn.input_data(shape=[None, 2]) net = tflearn.flatten(net) net = tflearn.reshape(net, new_shape=[-1]) net = tflearn.activation(net, 'relu') net = tflearn.dropout(net, 0.5) net = tflearn.single_unit(net)
def CharacterLSTM_Train(data, model, dictionary, history = 25, layers = 3, epochs = 10, hidden_nodes = 512, dropout = False): char_idx_file = dictionary maxlen = history char_idx = None ''' if os.path.isfile(char_idx_file): print('Loading previous char_idx') char_idx = pickle.load(open(char_idx_file, 'rb')) print("---------------") print(char_idx) print(len(char_idx)) ''' X, Y, char_idx = textfile_to_semi_redundant_sequences(data, seq_maxlen=maxlen, redun_step=3) pickle.dump(char_idx, open(dictionary,'wb')) tf.reset_default_graph() print("layers " + str(layers) + " hidden " + str(hidden_nodes)) ''' g = tflearn.input_data([None, maxlen, len(char_idx)]) for n in range(layers-1): g = tflearn.lstm(g, hidden_nodes, return_seq=True) if dropout: g = tflearn.dropout(g, 0.5) g = tflearn.lstm(g, hidden_nodes) if dropout: g = tflearn.dropout(g, 0.5) g = tflearn.fully_connected(g, len(char_idx), activation='softmax') g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) ''' g = buildModel(layers, hidden_nodes, maxlen, char_idx, dropout) m = tflearn.SequenceGenerator(g, dictionary=char_idx, seq_maxlen=maxlen, clip_gradients=5.0) #, checkpoint_path='model_history_gen') #if model is not None: # m.load(model) #for i in range(epochs): #seed = random_sequence_from_textfile(data, maxlen) m.fit(X, Y, validation_set=0.1, batch_size=128, n_epoch=epochs, run_id='run_gen') print("Saving...") m.save(model) #print("-- TESTING...") #print("-- Test with temperature of 1.0 --") #print(m.generate(600, temperature=1.0, seq_seed=seed)) #print("-- Test with temperature of 0.5 --") #print(m.generate(600, temperature=0.5, seq_seed=seed)) # inputs: # data - textfile # in_model - a TFLearn model file # outputs: # out_model - a TFlearn model file # params: # history - max length of sequence to feed into neural net # layers - number of hidden layers of the network # epochs - how many epochs to run # hidden_nodes - how many nodes per hidden layer
def patient_output(vector_rep_patient): # The vector representation for the patient sequence vector_rep_patient = convert_seq_to_vec(vector_rep_patient) # load the sc model sc = joblib.load('../Predictor_Tfidf/Saved_Models/Fully_Connected_n_epochs_10/standard.pkl') patient_seq = sc.transform(vector_rep_patient.toarray()) generate_icd9_lookup() # generate the lookup for each diagnosis for c, d in enumerate(uniq_diag): # Run each iteration in a graph with tf.Graph().as_default(): # Model input_layer = tflearn.input_data(shape=[None, 1391], name='input') dense1 = tflearn.fully_connected(input_layer, 128, activation='linear', name='dense1') dropout1 = tflearn.dropout(dense1, 0.8) dense2 = tflearn.fully_connected(dropout1, 128, activation='linear', name='dense2') dropout2 = tflearn.dropout(dense2, 0.8) output = tflearn.fully_connected(dropout2, 2, activation='softmax', name='output') regression = tflearn.regression(output, optimizer='adam', loss='categorical_crossentropy', learning_rate=.001) # Define model with checkpoint (autosave) model = tflearn.DNN(regression, tensorboard_verbose=3) # load the previously trained model model.load('../Predictor_Tfidf/Saved_Models/Fully_Connected_n_epochs_{0}/' 'dense_fully_connected_dropout_5645_{1}.tfl' .format(n_epoch, d)) # Standardize the values and predict the output vector_rep_patient_sc = np.reshape(patient_seq, (1, 1391)) # Find the probability of outputs Prediction_for_patient_prob[d] = np.array(model.predict(vector_rep_patient_sc))[:, 1] Prediction_for_patient[d] = np.where(Prediction_for_patient_prob[d] > 0.5, 1., 0.) print('Completed : {0}/{1}'.format(c + 1, len(uniq_diag))) # Print the predictions for the patient's input
