remove the log praser in tuning script

examples/librispeech/run_tune.sh

@@ -3,18 +3,18 @@
 pushd ../.. > /dev/null
 
 # grid-search for hyper-parameters in language model
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
 python -u tools/tune.py \
---num_batches=2 \
+--num_batches=-1 \
---batch_size=24 \
+--batch_size=256 \
 --trainer_count=8 \
 --beam_size=500 \
 --num_proc_bsearch=12 \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---num_alphas=2 \
+--num_alphas=45 \
---num_betas=2 \
+--num_betas=8 \
 --alpha_from=1.0 \
 --alpha_to=3.2 \
 --beta_from=0.1 \

examples/tiny/run_tune.sh

@@ -5,20 +5,22 @@ pushd ../.. > /dev/null
 # grid-search for hyper-parameters in language model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
 python -u tools/tune.py \
---num_samples=100 \
+--num_batches=1 \
+--batch_size=24 \
 --trainer_count=8 \
 --beam_size=500 \
 --num_proc_bsearch=12 \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
---num_alphas=14 \
+--num_alphas=45 \
---num_betas=20 \
+--num_betas=8 \
---alpha_from=0.1 \
+--alpha_from=1.0 \
---alpha_to=0.36 \
+--alpha_to=3.2 \
---beta_from=0.05 \
+--beta_from=0.1 \
---beta_to=1.0 \
+--beta_to=0.45 \
---cutoff_prob=0.99 \
+--cutoff_prob=1.0 \
+--cutoff_top_n=40 \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \

tools/tune.py

@@ -34,7 +34,6 @@ add_arg('beta_from',        float,  0.1,   "Where beta starts tuning from.")
 add_arg('beta_to',          float,  0.45,  "Where beta ends tuning with.")
 add_arg('cutoff_prob',      float,  1.0,   "Cutoff probability for pruning.")
 add_arg('cutoff_top_n',     int,    40,    "Cutoff number for pruning.")
-add_arg('output_fig',       bool,   True,  "Output error rate figure or not.")
 add_arg('use_gru',          bool,   False, "Use GRUs instead of simple RNNs.")
 add_arg('use_gpu',          bool,   True,  "Use GPU or not.")
 add_arg('share_rnn_weights',bool,   True,  "Share input-hidden weights across "
@@ -66,26 +65,9 @@ add_arg('specgram_type',    str,
 # yapf: disable
 args = parser.parse_args()
 
-def plot_error_surface(params_grid, err_ave, fig_name):
-    import matplotlib.pyplot as plt
-    import mpl_toolkits.mplot3d as Axes3D
-    fig = plt.figure()
-    ax = Axes3D(fig)
-    alphas = [ param[0] for param in params_grid ]
-    betas = [ param[1] for param in params_grid]
-    ALPHAS = np.reshape(alphas, (args.num_alphas, args.num_betas))
-    BETAS = np.reshape(betas, (args.num_alphas, args.num_betas))
-    ERR_AVE = np.reshape(err_ave, (args.num_alphas, args.num_betas))
-    ax.plot_surface(ALPHAS, BETAS, WERS,
-               rstride=1, cstride=1, alpha=0.8, cmap='rainbow')
-    ax.set_xlabel('alpha')
-    ax.set_ylabel('beta')
-    z_label = 'WER' if args.error_rate_type == 'wer' else 'CER'
-    ax.set_zlabel(z_label)
-    plt.savefig(fig_name)
 
 def tune():
-    """Tune parameters alpha and beta on one minibatch."""
+    """Tune parameters alpha and beta incrementally."""
     if not args.num_alphas >= 0:
         raise ValueError("num_alphas must be non-negative!")
     if not args.num_betas >= 0:
@@ -160,38 +142,36 @@ def tune():
             err_ave[index] = err_sum[index] / num_ins
             # print("alpha = %f, beta = %f, WER = %f" %
             #      (alpha, beta, err_ave[index]))
-            if index % 10 == 0:
+            if index % 2 == 0:
                 sys.stdout.write('.')
                 sys.stdout.flush()
 
         # output on-line tuning result at the the end of current batch
         err_ave_min = min(err_ave)
         min_index = err_ave.index(err_ave_min)
-        print("\nBatch %d, opt.(alpha, beta) = (%f, %f), min. error_rate = %f"
+        print("\nBatch %d [%d/?], current opt (alpha, beta) = (%s, %s), "
-                %(cur_batch, params_grid[min_index][0],
+              " min [%s] = %f" %(cur_batch, num_ins,
-               params_grid[min_index][1], err_ave_min))
+              "%.3f" % params_grid[min_index][0],
+              "%.3f" % params_grid[min_index][1],
+              args.error_rate_type, err_ave_min))
         cur_batch += 1
 
     # output WER/CER at every point
-    print("\nerror rate at each point:\n")
+    print("\nFinal %s:\n" % args.error_rate_type)
     for index in xrange(len(params_grid)):
-        print("(%f, %f), error_rate = %f"
+        print("(alpha, beta) = (%s, %s), [%s] = %f"
-              % (params_grid[index][0], params_grid[index][1], err_ave[index]))
+             % ("%.3f" % params_grid[index][0], "%.3f" % params_grid[index][1],
+             args.error_rate_type, err_ave[index]))
 
     err_ave_min = min(err_ave)
     min_index = err_ave.index(err_ave_min)
-    print("\nTuning on %d batches, opt. (alpha, beta) = (%f, %f)"
+    print("\nFinish tuning on %d batches, final opt (alpha, beta) = (%s, %s)"
-            % (args.num_batches, params_grid[min_index][0],
+            % (args.num_batches, "%.3f" % params_grid[min_index][0],
-              params_grid[min_index][1]))
+              "%.3f" % params_grid[min_index][1]))
-
-    if args.output_fig == True:
-        fig_name = ("error_surface_alphas_%d_betas_%d" %
-                   (args.num_alphas, args.num_betas))
-        plot_error_surface(params_grid, err_ave, fig_name)
-        ds2_model.logger.info("output figure %s" % fig_name)
 
     ds2_model.logger.info("finish inference")
 
+
 def main():
     print_arguments(args)
     paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)